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.

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.

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.

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.

Electrical power system monitoring, protection, operation, and control schemes are undergoing significant changes toward 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 Standard 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 Standard 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, this 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.

Partitioning of electric networks into zones or areas is a procedure that has numerous applications in power system planning, operation and control. Spectral clustering based approaches are among the most favoured ones to solve the partitioning problem. Applications of spectral clustering include definition of control zones, analysis of connectivity structure of power networks, intentional controlled islanding, design of sectionalising strategies, and visualisation. Although spectral clustering is a state-of-the-art family of methods with numerous extensions, some practical issues can arise when applying it to large-scale power networks. While spectral clustering becomes significantly more robust to outliers when combined with a robust post-processing method like k-medoids, the connectedness of the resulting partitioning cannot be guaranteed. This paper proposes a greedy algorithm to solve the connectedness issues inherent to many robust post-processing methods. Furthermore, it is proposed to utilise a label propagation based heuristic to improve the quality of the final partitions. The test results evaluate the steps of the methodology on a large-scale 1354-bus PEGASE test network.