Electrical power system monitoring, protection, operation, and control schemes are undergoing significant changes towards the next generation fully automated, resilient, and self-healing grids. At present there still exists a lack of available user-friendly tools for the Synchronized Measurement Technology supported application design. This paper presents a Synchro-measurement Application Development Framework (SADF) to promote a simplified design and thorough validation of synchro-measurement (IEEE Std. C37.118.2-2011) supported user-defined applications under realistic conditions. The proposed SADF supports online receiving of a Phasor Measurement Unit (PMU) or Phasor Data Concentrator (PDC) provided data stream and enables simultaneous use of processed machine-readable synchro-measurements in advanced user-defined applications. This paper fills the scientific gap between the IEEE Std. C37.118.2-2011 specifications and its implementation by proposing a novel robust communication technique and efficient synchro-measurement data parsing methodology. As a proof-of-concept, the proposed SADF is implemented as a novel open-source MATLAB library. Combining this library with MATLAB's signal processing and visualization functions allows mastering the design and validation of complex Wide Area Monitoring, Protection, and Control applications as well as PMU/PDC performance and compliance verification. Finally, the paper verifies the proposed library against the standard specifications, assesses its interoperability and performance via a cyber-physical simulation platform, and presents online voltage magnitude monitoring as an example application.

In an electric power system, slow coherency can be applied to identify groups of the generating units, the rotors of which are swinging together against each other at approximately the same oscillatory frequencies of inter-area modes. This serves as a prerequisite-step of several emergency control schemes to identify power system control areas and improve transient stability. In this paper, slow coherent generators are grouped based on the direction and the strength of electromechanical coupling between different generators. The proposed algorithm performs low-pass filtering of generator frequency measurements. It adaptively determines the minimal number of the measurements to be processed in an observation window, and performs data selectivity to prevent mixing of interfering coherency indices. Finally, it adaptively tracks grouping changes of slow coherent generators and determines a finite number of groups for an improved affinity propagation clustering. The proposed algorithm is implemented as an online MATLAB program and verified in real-time using RTDS power system simulator with the integration of actual synchronized measurement technology components as hardware-in-the-loop. The obtained results demonstrate the effectiveness of the proposed algorithm for robust and near real-time identification of grouping changes of slow coherent generators during the quasi-steady-state and electromechanical transient period following a disturbance. where he was involved in modeling SF6 circuit breakers. His current research interests include future power systems, large-scale power system transients, intelligent protection for future power systems, and wide-area monitoring and protection. Prof. Popov is a member of CIGRE. He has actively participated in WG C4.502 and WG A2/C4.39. He was a recipient of the IEEE PES Prize Paper Award and the IEEE Switchgear Committee Award in 2011. He is an Associate Editor of the International Journal of Electric Power and Energy Systems. Mart A. M. M. van der Meijden (M'10) received the M.Sc. degree (cum laude) in electrical engineering from the Eindhoven University of Technology, Eindhoven, The Netherlands, in 1981.He is leading research programs on intelligent electrical power grids, reliable, and large-scale integration of renewable (wind and solar) energy sources in the European electrical power systems and advanced grid concepts. He has more than 30 years of working experience in the field of process automation and the transmission and the distribution of gas, district heating, and electricity. Since 2003, he has been with TenneT TSO, Arnhem, The Netherlands, Europe's first cross-border grid operator for electricity, where he is the Manager of Research and Development/Innovation and was responsible for the development of the TenneT long-term vision on the electrical transmission system. He has been a Full Professor (part-time) with the

Today's power systems are seeing a paradigm shift under the energy transition, sparkled by the electrification of demand, digitalisation of systems, and an increasing share of decarbonated power generation. Most of these changes have a direct impact on their control centers, forcing them to handle weather-based energy resources, new interconnections with neighbouring transmission networks, more markets, active distribution networks, micro-grids, and greater amounts of available data. Unfortunately, these changes have translated during the past decade to small, incremental changes, mostly centered on hardware, software, and human factors. We assert that more transformative changes are needed, especially regarding humancentered design approaches, to enable control room operators to manage the future power system. This paper discusses the evolution of operators towards continuous operation planners, monitoring complex time horizons thanks to adequate real-time automation. Reviewing upcoming challenges as well as emerging technologies for power systems, we present our vision of a new evolutionary architecture for control centers, both at backend and frontend levels. We propose a unified hypervision scheme based on structured decision-making concepts, providing operators with proactive, collaborative, and effective decision support.

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Intentional controlled islanding aims to split the power system into self-sustainable islands after a severe disturbance, but prior the uncontrolled network separation. Given its nature (i.e. last resort for blackout prevention), this emergency control technique must be adopted as quickly as possible. This paper proposes a computationally efficient method based on graph reduction and spectral clustering. The paper contributes by describing important details of the graph reduction process in the context of controlled islanding and by the formalisation of this process. Furthermore, it demonstrates how to adopt embedded graphs to enhance the Multiway Spectral Clustering graph partitioning. Finally, it is shown how to explicitly incorporate important cannot-link constrains between coherent generator groups into the islanding problem. The proposed method is detailed using the IEEE 39-bus test case. To evaluate the algorithm performance, the method is applied to realistically-sized PEGASE test networks. 'case39'

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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 Electronic supplementary materialThe online version of this chapter (