DC fault location technology is crucial for estimating the fault location and developing multi-terminal direct current (MTDC) systems. This article presents a novel fault location method using the parameter fitting approach. The propagation of traveling waves (TWs) in the decoupled line-mode fault network is first discussed, resulting in analytical expressions for the backward line-mode current TWs containing fault location information. Then, the adaptive multi-step Levenberg–Marquardt (AMLM) algorithm is applied for parameter fitting owing to its fast processing speed and precision. The exact fault location is estimated using the fitted coefficient. Different testing MTDC systems modeled in PSCAD/EMTDC and a real-time digital simulator (RTDS) validate the proposed fault location method. Based on numerous simulation tests, the AMLM-based parameter fitting and the proposed method are accurate, with errors smaller than 0.5%. Compared to the existing methods, the proposed method has desired performance under close-in faults, can withstand 35 dB noise interference, and obviates the need for an extremely high sampling frequency, estimation of tws velocity, and communication devices.

The effective prediction of dynamic cable ratings (DCR) in the HVDC cable is pivotal for enhancing transmission efficiency and maximizing electricity sales in offshore wind farms. Due to complex wind conditions, traditional machine learning methods, such as support vector machines, struggle to provide accurate long-term DCR predictions and express prediction uncertainties. To address these challenges, this article proposes a novel deep learning framework for dynamic cable rating prediction based on encoder–decoder networks, in which the encoder utilizes Bidirectional extended-long Short-Term Memory networks to encode contextual information from the input data. The decoder introduces an additive attention mechanism, which allows the network to focus on relevant features in the input sequence. In addition, to capture the uncertainty for DCR prediction, a Bayesian neural network approximation method based on the Monte Carlo dropout method is introduced. Finally, this paper introduces a thermal risk estimation method by considering both the maximum conductor temperature limit and the temperature gradient limit. Results demonstrate that the proposed method not only improves electric field distribution but also achieves superior economic benefits.

The performance of existing protection methods for multi-terminal direct current systems depends on the availability and sizes of boundary components. To overcome the limitation, this paper proposes a non-unit DC line protection method based on the normalized backward traveling waves (BTWs) of the 1-mode voltage. Firstly, traveling wave propagation characteristics are analyzed, and a rationalization approach based on vector fitting is proposed. Next, the analytical expressions of normalized BTWs are derived, with the negative correlation between them and fault distance proved. Then, the derivative-free conjugate gradient algorithm is utilized for amplitude fitting and normalization calculation. Finally, a non-unit protection method using the normalized BTWs is developed. The performance is validated for both electromagnetic transient PSCAD/EMTDC and real-time digital RSCAD/RTDS simulation. The results demonstrate that the proposed method can accurately identify faults with various fault resistances and locations without requiring boundary components and high sampling frequencies, and it is robust against noise disturbances.

Power systems evolve towards more renewable and less conventional electricity supply. This, however, brings significant technical challenges, as conventional sources naturally provide system resilience. One of the key dimensions of this resilience is system strength, which is rapidly depleted with the phase-out of fossil-based synchronous generation. This paper commences by exploring the intricate steady- and dynamic-state aspects of system strength, and consequently elevated risks of voltage instability. A new holistic definition of system strength is further proposed. Considering the stability challenges of modern power systems, grid operators need to be aware of any vulnerable grid sections and dangerous operating scenarios to always ensure system security and stability. Nevertheless, the rising complexity of modelling and analysis of dynamics in modern power systems makes this task increasingly challenging. The large number of grid locations with complex inverter-based generation and load, paired with parameter uncertainty, make deterministic analytical analyses of voltage stability and system strength increasingly challenging and time-consuming. A novel data-driven voltage stability and system strength assessment method, termed Voltage Vulnerability Curves (VVCs), is hereby proposed to address these challenges. The method is designed to cut through the complexity of modern power systems’ dynamics and provide advanced system strength and voltage vulnerability insights.

HVDC cable systems are vital for long-distance power transmission, especially for offshore wind energy. The point-to-point HVDC transmission links are already in use. For enhanced system reliability, interconnected DC network with fault separation devices like DC circuit breakers are envisaged. This requires the evaluation of the interface between DC cables and circuit breakers for different contingency scenarios. Electromagnetic transient simulations are commonly employed for HVDC network analysis. This paper proposes a practice-based approach to accurately model cable systems, which are crucial for reliable HVDC network analysis. It emphasizes considering real-world implications of cable manufacturing and installation conditions. By enhancing existing knowledge of cable parameter determination, this study proposes practical models using EMT simulations. The aim is to provide engineers with a systematic method to convert DC cable data into EMT-compatible parameters, facilitating representative modelling for thorough HVDC network performance assessment. This is vital for de ning speci cations of complex HVDC systems, particularly multi-terminal networks.

There is a lack of knowledge on the development of a controller model into a black box dynamically-linked library (DLL) file which can be flexibly tested with a power system model through a real-time co-simulation platform. Developing a test bed to demonstrate the performance and compliance analysis enhances the approval of novel control and protection strategies. This paper presents the development of a controller DLL and a test bench that integrates this DLL with a real-time simulator. The DLL contains a previously developed grid-forming controller for the type‑3 wind turbine and works in co-simulation with the power system model on Real-Time Digital Simulator (RTDS) in real-time.

Unlike synchronous generators, the fault response of grid-forming (GFM) inverter-interfaced distributed generators (IIDGs) is notably governed by the selection of control and current limiting strategies rather than inherent physical traits. While recent research has focused on the sequence domain fault model of GFM IIDGs, a research gap exists in elucidating the influence of control and current limiting schemes on this model's characteristics. This article aims to fill this void by examining how different control and current limiting schemes influence the positive and negative sequence impedances in the phasor-domain fault model of GFM IIDGs. This investigation encompasses droop-based, virtual synchronous machine-based, and virtual oscillator-based reference generation controls alongside rotating and stationary reference-frame-based voltage controls. Furthermore, saturation-based, latching-based, circular and virtual impedance-based current limiting schemes are analyzed. To achieve this goal, a thorough numerical simulation study is conducted. Findings indicate that outer reference generation controls exhibit minimal impact. Conversely, the choice of voltage control and various current limiting schemes emerge as the predominant factors shaping the sequence models of GFM IIDGs. These analyses and results are instrumental in devising reliable protection strategies within inverter-based grids, as a comprehensive understanding of electrical elements in the sequence domain is imperative for effective protective measures.

This paper deals with the essentials of synchrophasor’s applications for future power systems to increase system reliability and resilience, which have been investigated within a four-year research project. The project has several applications, covering real-time disturbance detection and blackout prevention distributed across multiple work-packages. Firstly, an advanced big-data management platform built in a real-time digital simulation (RTDS) environment is described to support measurement data collection, processing, and sharing among stakeholders. This platform further presents and demonstrates a network-splitting methodology to avoid cascading failures. Online generator coherency identification is another synchrophasor application implemented on the platform, the use of which is demonstrated in the context of controlled network splitting. Using synchrophasors, data-analytics techniques can also identify and classify disturbances in real time with minor human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system is outlined to detect and classify critical events. Finally, the paper elaborates on developing advanced system resilience metrics for real-time vulnerability assessment of power systems with a high penetration of renewable energy, focusing on increasingly relevant dynamic interactions and system instability risks.

In the dc traction power supply system in subway, over-current tripping device (OCTD) is essential for air circuit breakers as backup protection. At present, current threshold value (I _th) of OCTD is adjusted by the cooperation of magnetic circuit regulating block (MCRB) and spring. Depending on MCRB is installed or not, only two-level adjustment can be realized, resulting in low adjustment accuracy of I _th. In this letter, a novel OCTD based on controllable magnetic coupling is proposed. Instead of using MCRB, an auxiliary winding wrapped on the static iron core is added, with different resistors and thyristors attached. By controlling thyristors, a wide-range and multilevel adjustment of I _th can be achieved. Benefits of the proposed method are: reducing the stiffness factor of spring from 55 to 42 N/mm, improving the adjustment accuracy from 0.66 to 0.33 kA/mm, and adjusting I _th online without power supply system interruption.

Anticipating failures is vital for maintaining a reliable power supply. Advanced measurement devices in the grid generate vast data that contains valuable information on grid operations. Initial signatures of an incipient failure are often reflected in this data in the form of electrical waveform distortions. Conventional protection schemes are not equipped to analyze these distortions and anticipate failures. There is a considerable research gap for a simple yet robust and universal failure anticipation and diagnosis scheme. This paper proposes a universal Failure Anticipation and Diagnosis Scheme (FADS) to detect incipient failures in AC distribution grids. The method comprises three short stages, helping the operator make an informed decision. In the first stage, the FADS scheme leverages the fundamental properties of electrical sinusoid waveforms to detect distortions. In the second stage, the distortion data is processed through pre-determined thresholds set in accordance with the system's regular operation. In the third stage, depending on the system, the FADS uses the extent of the violations of these thresholds and ranks the severity of the danger posed to grid operations. The classification helps determine if the waveform distortions are the signature of an incipient failure. The proposed FADS method's reliability, robustness and effectiveness are evaluated in incipient failure conditions of field events modelled in real-time simulations on standardized IEEE distribution feeders. The FADS is a high-speed distortion detector, is quite sensitive, and the method has high selectivity because of its nature.

Wavelet transform has proven to be a capable tool for protection purposes in high voltage direct current (HVDC) transmission lines due to its desired speed and accuracy. However, the need to enhance the WT-based protection methods in terms of sensitivity and selectivity is of interest. This paper proposes a new non-unit WT-based protection method with adaptive threshold setting. According to the improved time-domain analytical approach, line-mode fault-generated voltage traveling wave is adopted to identify the internal faults. The simulation results for a multi-terminal modular multilevel converter-based HVDC grid in PSCAD/EMTDC corroborate accurate and fast internal faults detection of the proposed method, up to 850 Ω, i.e., almost three times larger than conventional schemes. In addition, the reliable performance of the presented method in a noisy environment, using relatively low sampling frequencies, and different sizes of current limiting inductors is demonstrated in the presented analysis. The generality of the presented analytical approach ensures that the proposed protection method can be extended to more complex HVDC grids.

The rapid increase of integrated distributed generators results in higher fault currents in the future modern grids. A remedy for the concern is employing series reactors as fault current limiters. This paper elaborates on a ferromagnetic core series reactor, which, when saturated, adversely affects the operation of the series reactor during faults. The main goal of the paper is to calculate grid and series reactor coefficients by applying a simplified power line model during a fault condition. These coefficients are the primary considerations of a series reactor design to avoid its saturation. Moreover, the study of the relationship between the reactor inductance and obtained coefficients will be carried out. The obtained results are validated by simulations performed in MATLAB Simulink.

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.

The integration of renewable energy sources has significantly impacted power system protection schemes, primarily by increasing short-circuit fault currents, which, in turn, raises the possibility of current transformer (CT) saturation, and by introducing bidirectional fault current flow, which interferes with the directional selectivity of relays in detecting downstream faults. This study presents a novel fault direction identification algorithm aimed at immunization against CT saturation effects, especially in medium voltage (MV) distribution grids integrated with renewable sources. To achieve this, two distinct computational frameworks have been developed and proposed. The first framework utilizes the modified least squares (MLSs) method, while the second employs a modified Kalman filter (MKF). Both algorithms calculate the fundamental current phase angle using a sub-cycle window of current samples, ensuring resilience to heavily distorted waveforms caused by CT saturation, even under conditions of deep saturation. The effectiveness of the proposed method is validated through numerous tests conducted on fault currents recorded via simulation scenarios and field measurements, considering various fault inception times, resistances, and locations, together with different neutral grounding arrangements. Comparative assessments of the two developed frameworks across different scenarios indicate that both methods exhibit promising performances, although the least square-based method demonstrates superior efficiency compared to the Kalman filter-based method.

Effective islanding detection is mandatory for distributed generations (DGs) to avoid equipment damage and ensure the safety of network personnel. This paper proposes a fast, accurate, power quality-friendly, and practical two-stage active power curtailment (APC)-based islanding detection technique (IDT) for photovoltaic (PV)-rich microgrids. In the first stage, a periodic small disturbance is injected into the maximum power point tracking (MPPT) algorithm to slightly curtail the DG's active power, causing a small mismatch even on a balanced island. During islanding, the introduced active power mismatch shifts output voltage, triggering the second stage disturbance to the MPPT algorithm. Hence, the output voltage drops further, resulting in islanding detection. A real-time digital simulation (RTDS) using the modified IEEE 13-node system corroborates the successful detection of all stringent cases in less than 1.2 s with no false tripping for non-islanding disturbances. This zero non-detection zone (NDZ) is achieved by curtailing less than 1 % of the DG's available output. This technique is a practical solution for microgrids with high penetration of photovoltaic generators (PVGs) due to its simple structure and straightforward threshold determination, irrespective of the microgrid structure. The fast detection time allows the DG to seamlessly transition to the standalone microgrid.

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.

This paper presents an analytical framework for understanding DC fault current interruption dynamics using fundamental electrical circuit principles. It examines the interruption mechanisms of a DC circuit breaker with a detailed HVDC cable model through Electromagnetic Transient (EMT) simulations. The research integrates analytical and numerical simulation approaches during fault conditions to assess varying parameters for the impact of DC fault current interruption on estimating the resulting cable stresses. This research aims to advance HVDC transmission technology by providing a com-prehensive understanding of DC fault current interruption dynamics of cable-based networks.

The protection of DC systems in mobility applications, such as land transport, aircraft, and shipping, presents significant challenges due to the need for high power density equipment in confined spaces. This paper focuses on DC systems on board ships, for which diverse applications require different power levels, architectures, and protection strategies. Existing protection frameworks and regulations are often inadequate or outdated for the field, leading to certification issues and insufficient fault analysis. This research proposes a use case-based categorization of short circuit currents for primary systems. A reference scenario is created using a simulation model of a 5 MW system in a superyacht to provide a short circuit inventory. The study proposes three contributions. A comprehensive fault inventory, a qualitative categorization, and relevant recommendations for power converter design. The research highlights the importance of fault categorization in understanding the impact of various short circuits on shipboard DC systems. The study emphasizes the importance of the evolution of materials and power converters in developing efficient protection technologies for ships. This work addresses some fundamental gaps in shipboard DC systems, providing a foundation for improved protection strategies and regulations, ultimately contributing to the advancement of protection of shipboard DC systems.

Power transformer energization involves a significant electromagnetic energy exchange among system components, which periodically oscillates with the natural frequencies of the system. As a result, weakly damped resonating overvoltages (OVs) may prevail, overstressing the system and thus leading to potential insulation failure. This phenomenon is particularly notable in cable-transformer systems, where the coinciding of natural frequencies is more likely to occur. The prestriking phenomenon during circuit breaker (CB) closing plays an important role in the excitation of the resonance frequencies. Specifically, repeated prestrikes can create high-frequency resonances during switching-on operations. This paper deals primarily with the mutual interactions between cable and transformer during transformer energization. Furthermore, by using a suitable model, the impact of CB prestrikes on the resultant resonance excitation is analyzed. Results demonstrate that cable-transformer interactions can lead to high-frequency oscillatory OVs with extreme magnitudes.