With the growing number of severe system disturbances and blackouts around the world, controlled system separation is becoming an increasingly important system integrity protection scheme (SIPS) to save the electric power system from a complete or partial disintegration. A successful controlled splitting approach should at least tackle the following two well-known and interrelated problems: 'when to split?' and 'where to split?'. Multiple previous publications consider these problems separately, and even those pursuing a combined approach propose solutions with limited applicability. In this article, we are proposing a novel PMU-based detector of loss of synchronism that utilizes generator PMU data to promptly detect rotor angle instabilities over a wide area. Moreover, we are showing how our loss of synchronism detection principle can be coupled with the known controlled splitting techniques to form an integrated defense scheme against unintentional loss of synchronism. The performance of this wide-area SIPS is demonstrated on the IEEE 39-bus test power system for various types of unstable conditions.

This paper presents a new formulation for intentional controlled islanding (ICI) of power transmission grids based on mixed-integer linear programming (MILP) DC optimal power flow (OPF) model. We highlight several deficiencies of the most well-known formulation for this problem and propose new enhancements for their improvement. In particular, we propose a new alternative optimization objective that may be more suitable for ICI than the minimization of load shedding, a new set of island connectivity constraints, and a new set of constraints for DC OPF with switching, and a new MILP heuristic to find initial feasible solutions for ICI. It is shown that the proposed improvements help to reduce the final optimality gaps as compared to the benchmark model on several test instances.

This chapter presents a general overview of the experience learned with the use of DIgSILENT PowerFactory in the design of theoretical lectures and practical sessions of a power system dynamics course at postgraduate level. This chapter focuses on the experiences acquired in the course that is part of the MSc program in Electrical Engineering of TU Delft, Department of Electrical Sustainable Energy. The discussion provided in this chapter focuses on power systems application with special focus on (i) Steady-state, Dynamic, (ii) Voltage Stability and (iii) rotor angle stability. The main objective of using PowerFactory at MSc level is to expose the postgraduate students to real-life application, however, without lack of generalisation this chapter is dedicated to the is to expose to the application above by using a very well-known two area-four machine test power system (2A4G), it gives students insights and experience with cases closer to actual power systems. Results of this pedagogical experience demonstrate the importance of incorporating appropriate power system simulations tools in the postgraduate level.

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.

An increasing number of methods for control and analysis of power systems relies on representing power networks as weighted undirected graphs. Unfortunately, the presence of outliers in power system graphs may have a negative impact on many of these methods. In addition, detecting outliers can be a relevant task on its own. Motivated by the low number of outlier detection algorithms focusing on weighted undirected graphs, this paper proposes an efficient and effective method to detect loosely connected graph clusters below a certain number of nodes. The essence of the method lies in the efficient examination of the spectral minimal spanning tree of the input graph. The obtained results on several large test power networks validate the high outlier detection performance of the proposed method and its high computational efficiency.

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.

This paper presents an integrated approach to partition similarity graphs, the task that arises in various contexts in power system studies. The approach is based on orthogonal transformation of row-normalized eigenvectors obtained from spectral clustering to closely fit the axes of the canonical coordinate system. We select the number of clusters as the number of eigenvectors that allows the best alignment with the canonical coordinate axes, which is a more informative approach than the popular spectral eigengap heuristic. We show a link between the two relevant methods from the literature and on their basis construct a robust and time-efficient algorithm for eigenvector alignment. Furthermore, a graph partitioning algorithm based on the use of aligned eigenvector columns is proposed, and its efficiency is evaluated by comparison with three other methods. Lastly, the proposed integrated approach is applied to the adaptive reconfiguration of secondary voltage control (SVC) helping to achieve demonstrable improvements in control performance.

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.