Inrush current refers to the high-magnitude excitation current drawn by a transformer upon energization. The intensity of inrush current is a function of the residual flux of the transformer's core and the voltage magnitude at the energization instant. This paper proposes a method to effectively mitigate the inrush current of single-phase transformers. The proposed method overcomes the shortcomings of the well-established pre-fluxing method, and thus, is referred to as the modified pre-fluxing method. The method operates without requiring any prior knowledge regarding the transformer's design information or parameters. Accounting for uncertainties in circuit breaker closing operation, the core's residual flux is modified to an appropriate reference value, minimizing the corresponding adverse impact. The flux adjustment is accomplished by a power electronic circuitry that applies suitable voltage across the transformer's low-voltage winding. The core's residual flux is estimated after removing the DC offset present in the measured open-circuit voltage. The energization process is then initiated at an appropriate instant ensuring the core's steady-state flux matches its adjusted residual flux. The efficiency of the modified pre-fluxing method is demonstrated by conducting 12,000 simulations in PSCAD/EMTDC. A hardware-in-the-loop (HIL) setup composed of a transformer and pre-fluxing device is used for extensive experimental validation and comparison with recent energization methods under more realistic conditions.

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.

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.

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.

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.

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.

Dealing with the fast-rising current of high voltage direct current (HVdc) systems during fault conditions, is one of the most challenging aspects of HVdc system protection. Fast dc circuit breakers (DCCB) have recently been employed as a promising technology and are the subject of many research studies. HVdc circuit breakers (CBs) must meet various requirements to satisfy practical and functional needs, among which fast operation, low voltage stress, and economic issues are the key factors. This article presents the procedure for designing a superconductive reactor-based DCCB (SSR-DCCB) for HVdc applications. In the proposed structure, a full-bridge power electronic configuration controls the superconducting reactor to limit the dc fault current and create a dc zero-crossing; it is connected to the HVdc line by a series transformer. After successfully suppressing the line fault current (current zero current), an ultrafast disconnector isolates the faulty line. The main advantage of the proposed HVdc CB is its ability to interrupt the dc fault current without using the solid-state main breaker and limit the magnitude of the fault current and voltage stress. The proposed SSR-DCCB is investigated in MATLAB/Simulink, and an experimental prototype setup validates the results.

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.

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.

This paper investigates the high-frequency transformer losses attributed to eddy currents in both conductors and the core. A comprehensive model of a transformer winding is presented, meticulously incorporating skin and proximity effects. The winding resistance increase due to these effects is determined by applying analytical and finite element methods. The investigation highlights that eddy current losses in the magnetic core notably increase the resistance of each winding section in low frequencies. However, the impact of these losses diminishes as the winding length approaches the voltage wavelength at high frequencies. Consequently, the net magnetomotive force and flux in the core become negligible. As a result, the high-frequency impedance characteristics are remarkably similar for windings with and without a magnetic core. These findings are substantiated through rigorous simulations and empirical measurements.

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.

The stability of an HVDC transmission network is very important for the reliable transfer of power from renewable energy resources. Therefore, this paper proposes a region of attraction stability analysis method for grid-forming-based modular multilevel converters. The grid forming control uses a model predictive control-based controller for the inner current loop. The stability analysis is carried out using the direct Lyapunov method. For the model predictive control, the modified version of the cost function is used; the same function is also used as the cost function for the direct Lyapunov method. Furthermore, the boundaries formed by the limiters and Lyapunov function entirely explain transient event behavior.

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.

An economical approach for incorporating Battery Energy Storage Systems (BESS) onto DP-2 vessels is presented in this research. The paper deals with developing a Battery Optimization for Optimal Sizing and Throughput Energy Regulation (BOOSTER) framework for putting research findings into practice by optimizing battery size, technology choice and power generation scheduling while considering battery degradation. Twelve battery sizes are analyzed based on three key performance metrics: return on investment, payback period, and years of profitability. A Mixed Integer Linear Programming (MILP) is developed to operate the energy and power management system of the vessel in a fuel and economically efficient manner. The study considers two load profiles of a DP-2 vessel operating near Taiwan and the North Sea. Our findings emphasize the significance of taking battery ownership costs in the form of energy throughput cost and fuel price into account, resulting in a longer battery lifetime and higher return on investment. The research also proposes a BESS operation matrix that provides vessel operators with valuable information on BESS usage for economic benefits. This matrix translates analytics and decision-making into tangible actions that can be implemented in real-time operations. Based on the findings, energy systems may be optimized for a sustainable future, which benefits vessel operators and industry stakeholders.

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.