Cross-country faults can adversely affect the operation of protection devices. Relays whose pickup is based on overcurrent or impedance elements are more likely to fail during cross-country faults. The failure can be expressed as an unnecessary trip of all phases during a single-phase fault or even blocked relay functions. Relays may also fail to operate due to low fault currents, which is the case when power grids make use of many inverter-based resources (IBR) or when two simultaneous faults occur in the same phase and in different locations. This paper proposes a Recursive Discrete Stockwell Transform (RDST) method for addressing the mentioned challenges. The energy content computed by the RDST is used for fault detection and faulty phase selection. A robust distance relay model is proposed to ensure correct relay performance and is combined with a directional and an impedance trajectory module. The proposed method performance is thoroughly evaluated by applying real-time simulations for 6480 different cross-country faults, including three repetitions, four different generators (Synchronous generators, Wind turbine type-III, Wind turbine type-IV), two rating powers, three different fault distances, five different types of faults, and six grid codes. Ninety cases are also tested with real devices in hardware in the loop. Simulation results confirm the method's ability to detect different fault types, even during low fault currents, and to ensure high accuracy in phase selection.@en

The modernization of electrical power systems is reflected through the integration of renewable energy resources, with the ultimate aim of creating a carbon–neutral world. However, this goal has brought new and complex challenges for the power system, with one of the most crucial issues which is the reduction of system inertia. The decrease in system inertia has led to severe difficulties in maintaining frequency stability. As a result, power system operators must continuously monitor the system inertia and when necessary to activate appropriate preventive measures, ensuring a reliable and secure operation of the power system. Fortunately, wide-area monitoring systems can provide the necessary measurements to monitor and analyze system behavior, assisting system operators in undertaking optimal actions. This paper provides a review of recent publications that apply machine learning (ML)-based methods for monitoring power system inertia. It also provides an overview of academic and industrial projects related to ML-based methods for inertia monitoring. Furthermore, the paper explores applications based on ML-based methods and inertia. Lastly, the paper briefly discusses future directions for the development of this research field.

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Increasing penetration of inverter-based resources (IBRs) undermines the performance of conventional protection systems since IBRs’ fault characteristics are fundamentally different from those of synchronous generators. In this paper, a PMU-based backup protection method is proposed for power transmission systems with high penetrations of IBRs. The method involves replacing all IBRs and the line suspected to be faulty by proper nodal current sources. For accurately detecting the faulted line from the set of candidates, a residual-based index is proposed, utilizing the concept of superimposed circuits and the weighted least-squares method. The method's robustness in the face of influential factors, including input errors, settings, numbers, and locations of IBRs is investigated through extensive simulations on the IEEE 39-bus test system.@en

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.

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In this paper, a real-time state estimation platform for distribution grids monitored by Phasor Measurement Units (PMUs) is developed, tested, and validated using Real Time Digital Simulator (RTDS). The developed platform serves as a proof-of-concept for potential implementation in an existing 50 kV ring network of the Dutch distribution utility Stedin medium voltage distribution grid located in the southwest (Zeeland area) of the Netherlands. To catch up with the fast sampling rates of PMUs, the platform incorporates computationally efficient techniques for state estimation and detection, discrimination and identification of anomalies like bad data and sudden load changes. Forecasting Aided State Estimation has been utilized to enable measurement innovations needed for fast anomaly detection, discrimination, and identification, whilst the Extended Kalman Filter (EKF) algorithm is selected to provide fast state forecasting and filtering. The platform has been tested under various normal and abnormal operating conditions considering different statistical properties of measurement noise as well as different bad data and sudden load change scenarios. To demonstrate advantages and disadvantages for embedding EKF into the platform, EKF is compared with Unscented Kalman Filter (UKF) in terms of estimation accuracy, computational efficiency, and compatibility with the module for anomaly detection, discrimination, and identification. The results of extensive simulations provide good hints about the feasibility of PMU-based real-time state estimation for the Stedin distribution grid.@en

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.

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Local protection elements such as fuses and relays are the first protective mechanism to clear the fault and isolate the affected part of the power grid. Although the selectivity, speed, and sensitivity of these primary protection devices are relatively high, they cannot be considered flawless. There is a small percentage of events for which relays experience blinding effects. For these scenarios, a redundant arrangement can be made through backup protection. This paper proposes a centralized remote backup protection method based on two techniques, the delta algorithm and the least-squares technique. The proposed method successfully detects the faulted transmission line, fault type, and the distance to the fault. Besides, it makes use of phasor measurement unit data and it is non-iterative. The grid is split in a user-determined number of subareas based on the phasor mesurement unit locations, in order to accurately determine the fault location. Firstly, the faulty area is located and thereafter an in-depth search is carried out on the faulted area to determine the faulted line. Finally, the fault distance is determined based on the distributed parameter model of the transmission line. The method is demonstrated and validated in an RTDS-Matlab co-simulation platform. Extensive simulation studies are carried out on the IEEE 39-bus system to validate the proposed method.@en

This paper presents a new fault detection algorithm based on the Fast Discrete Stockwell Transform. The algorithm can improve the functionality of existing distance protection and resolve shortcomings identified during the fault detection process in case of fault occurrence in systems with a high penetration of power electronics-based generators. Reported results of the operation of commercial distance relays of four different vendors show that all relays experience difficulties during ungrounded faults. An RTDS testbed is developed for extensive hardware in the loop testing, comprising electromagnetic transient models of Type-3 and Type-4 wind generators. The proposed algorithm successfully overcomes the identified problems for cases where commercial relays maloperate. The threshold parameters for the fault detection are set by using the energy content attributed to the Fast Discrete Stockwell Transform time–frequency domain signal. Other distance protection modules such as the determination of directionality, phase selection and the computation of the impedance, which are necessary for the protection selectivity, are developed based on currently available solutions applied in commercial relays. The new algorithm has been extensively tested using RTDS for various fault conditions and the obtained results are reported in the paper.@en

The conventional Under Frequency Load Shedding (UFLS) scheme could result in unacceptably low frequency nadirs or overshedding in power systems with volatile inertia. This paper proposes a novel UFLS scheme for modern power systems whose inertia may vary in a wide range due to high penetration of renewable energy sources (RESs). The proposed scheme estimates the rate of change of frequency (RoCoF) of the center of inertia (CoI), and consequently, the loss of generation (LoG) size, using local frequency measurements only. An innovative inflection point detector technique is presented to remove the effect of local frequency oscillations. This enables fast and accurate LoG size calculation, thereby more effective load shedding. The proposed UFLS scheme also accounts for the effect of the inertia change resulting from LoG events. The performance of the proposed scheme is validated by conducting extensive dynamic simulations on the IEEE 39-bus test system using Real Time Digital Simulator (RTDS). Simulation results confirm that the proposed UFLS scheme outperforms the conventional UFLS scheme in terms of both arresting frequency deviations and the amount of load shed.@en

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.

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This paper deals with a comprehensive analysis to test the performance of available distance protection, that can be used in transmission networks with high penetration of converter-based distribution generation. For this, a new benchmark model has been developed according to the European Network of Transmission System Operators (ENTSO-e) guidelines. A 400 kV transmission network has been modeled in detail by taking into account wind turbine (WT) Type-3, Type-4 and PV generation as well as conventional generation where applicable. The network also makes use of a point-to-point HVDC connection, and it can be used to simulate variable penetration of distribution generation up to 100%. The system is modeled in details by making use of EMT models developed in RTDS environment. Numerous tests have been performed for protective relays from different vendors and different grid code constraints. The performance of the different vendor distance relay is analyzed for different fault types on transmission line. Further, a transient-based hybrid scheme has been discussed that is capable of operating under challenging conditions. The work is realized in the frame of the large EU Horizon 2020 project MIGRATE to study the performance of power system protection with large penetration of power electronic devices.@en

Identifying generator coherency with respect to slow oscillatory modes has numerous power system use cases including dynamic model reduction, dynamic security analysis, or system integrity protection schemes (e.g., power system islanding). Despite their popularity in both research and industry, classic eigenvector-based slow coherency techniques may not always return accurate results. The multiple past endeavors to improve their accuracy often lack a solid mathematical foundation. Motivated by these deficiencies, we propose an alternative consistent approach to generator slow coherency. Firstly, a new approach is introduced to accurately detect slow coherent generators by effectively minimizing generic normalized cuts. As a by-product, the new approach can also guide the choice of the number of slow coherent groups. Secondly, it is shown that the combination of the the proposed slow coherency approach and an enhanced version of the inertial generator aggregation method allows to produce accurate dynamic equivalents even if the selected number of generator groups is relatively low.@en

Short-circuit faults occurring close to either end of a transmission line are normally cleared with some time delay by the distance relay at the opposite end of that line. The pilot relaying schemes require communication media in order to reduce this time delay. This paper presents a noncommunication method that provides high-speed distance relaying over the entire length of transmission lines. Similar to conventional distance relays, the proposed method requires voltage and current signals at the relay location as well as the impedance parameters of the protected line as input. Accelerated sequential tripping (AST) for faults on the end-sections of the line is achieved by using the signals measured from the fault inception to several cycles after the opening of the remote-end circuit breaker (ORCB). A formula is put forward for determining the exact fault distance by using post-ORCB signals. Two indices are also proposed for detecting three-and single-pole ORCB in order to fulfill the prerequisite for accurate fault location and generating a trip command, if needed. The proposed method is validated by conducting more than 10000 simulations on the 39-bus test system using DIgSILENT PowerFactory.

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Short-circuit faults close to either end of a transmission line, are normally cleared instantaneously by the distance relay at that end and after hundreds of milliseconds, i.e., in Zone 2 operating time, by the relay at the opposite end of the line. This sequential tripping can be accelerated on condition that a reliable communication link is available for signaling between the two line ends. This paper proposes a novel non-communication method providing high-speed distance relaying over the entire length of the protected transmission line. The inputs to the method are the protected line parameters and local voltage and current signals measured by the relay, similar to those to conventional distance relays. The proposed method accomplishes Accelerated Sequential Tripping (AST) within a couple of cycles after the opening of the remote-end circuit breaker (ORCB) of the line. To achieve this, an accurate closed-form solution is derived for the fault distance in terms of post-ORCB voltage and current phasors. For the detection of the ORCB instant, a set of proper indices are proposed. This is to verify the fault distance calculated by the relay, before issuing a trip command. The proposed method is successfully validated by conducting more than 20000 hardware-in-the-loop (HIL) tests, and also using real-life data.@en

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.

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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.

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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.

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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.

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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.

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The motivation to upgrade the traditionally designed and operated electric power systems (BPS) with the state-of-the-art Smart Grid technology is driving the research and industry towards advanced Wide Area Monitoring, Protection and Control (WAMPAC) systems. As the performance of corrective control algorithms is often mission critical, it is prudent to extensively validate the emerging grid-saving applications in a safe and isolated environment before they are implemented in an actual BPS. In this paper, an experimental cyber-physical simulation platform is presented, as a co-simulation between the Synchronized Measurement Technology (SMT) supported BPS model and the underlying ICT infrastructure in real-time. The paper presents a WAMPAC-ready test platform, used for online assessment and evaluation of emerging closed-loop corrective control algorithms. The paper describes the conceptual architecture of the platform the implemented hardware and software components and, as a proof-of-the concept, presents some of the implemented example applications.@en