This study examines transient overvoltage phenomena in 525 kV high-voltage direct current (HVDC) onshore cable systems, with particular emphasis on the influence of grounding configurations in two joint types: straight-through and screen-separated. Transient overvoltages arising from wave propagation and reflections are analysed, highlighting the impact of joint types, bonding cable configurations (coaxial vs. noncoaxial) and bonding cable length on the resulting overvoltage magnitudes. The necessity of modelling screen-to-earth representations of sectionalised cables at grounded joint locations in the vicinity of faults is emphasised, whereas simplified representations of ungrounded and grounded straight-through joints are identified as sufficient for system-level simulations. To address the computational challenges of detailed electromagnetic transient simulations, a stand-alone simplified circuit is proposed to analyse grounded joint transients and to mitigate errors caused by insufficient time-step resolution. The results provide practical insights for insulation coordination, supporting the reliable integration of HVDC technology into long-distance cable-based transmission networks while enhancing system resilience.

Battery energy storage systems (BESSs) have been used in AC Microgrids (AMGs) for frequency control (FC) and energy management (EM). AMGs with low inertia might suffer large frequency deviations with high rates without the required reserve power for FC. This paper proposes a linear model for the optimal operation of grid-connected AMGs considering frequency security constraints. BESS and photovoltaic systems both participate in primary FC (PFC) and EM. PVSs can decrease their generation in power surplus conditions. They can release the energy of their DC-link capacitors in power shortage conditions. Through coordinated use of BESS and PVSs, the required BESS power for PFC decreases considerably, which allows the BESS to participate in EM more effectively and hence reduces the AMG operational cost. Frequency simulation studies show that PVSs can considerably assist BESS for PFC. Moreover, the optimization results show that without PVSs' support, load shedding is unavoidable which increases the AMG operation cost significantly. In this regard, deterministic and stochastic optimization show that PVSs' participation in PFC results in 24 % and 24.2 % reduction in the AMG operation cost compared to those when BESS is only used for PFC. Therefore, the PVSs' assist in PFC, even though short, has large impact on the optimal operation of the AMG.

Power transformer energization involves a significant electromagnetic energy exchange among system components, with periodic oscillations at the system's natural frequencies. As a result, weakly damped resonance overvoltages may occur, overstressing the system and thereby leading to potential insulation failure. This phenomenon is particularly notable for topologies where a transformer is supplied via cable, as low-damping resonance frequencies are likely to be formed due to mutual interactions between the cable and the transformer. The prestriking phenomenon during circuit breaker closing plays an important role in the excitation of resonance frequencies. Specifically, repeated prestrikes can create highfrequency resonances during switching-on operations. This paper analyzes the mutual interactions between the cable and the transformer, focusing on how the resonances between the cable and the transformer are created. Then, using a suitable modeling approach, the impact of CB prestrikes on the resultant resonance excitation in cable-transformer systems is investigated. Finally, tests are conducted using an experimental test setup to validate the investigations performed. The obtained results demonstrate that resonance frequencies emerging from cable-transformer interactions lead to the excitation of oscillatory overvoltages with extreme magnitudes.

Increasing wind farm capacity via overplanting enhances energy production but risks accelerating cable aging if transmission capacity is poorly managed. Consequently, resilient Dynamic Cable Rating (DCR) prediction-defined as the ability to maintain stability under data quality degradation and operational shifts-is crucial for reliable operation. However, achieving this is challenged by limited datasets, missing data, and complex spatio-temporal correlations. To address these issues, a resilient DCR prediction and thermal estimation framework is developed. First, a Conditional Generative Adversarial Network (CGAN) is applied to synthetically augment limited datasets, effectively resolving the load data imbalance. Second, a Spatio-Temporal Graph Attention Residual Shrinkage Network (STGARSN) is proposed. This model integrates an extended Long Short-Term Memory (LSTM) network with Temporal Convolutional Networks (TCN) and a graph attention mechanism to capture complex correlations. Crucially, it incorporates a residual shrinkage module to filter noise and outliers, thereby ensuring model resilience. Finally, to optimize economic performance while minimizing cable aging, a comparative analysis of various overplanting strategies is conducted. Experiments on real cable temperature measurements demonstrate the superior resilience of the proposed model, maintaining high accuracy not only across different forecasting horizons but also under conditions of missing data and sensor noise. The proposed framework accurately predicts DCR and supports long-term offshore wind farm operations through improved economic and technical decision-making.

As the energy sector transitions toward increased renewable integration and bidirectional grid operation, the complexity and frequency of disturbance events rise, necessitating advanced situational awareness. Robust expert systems are needed that leverage a multilayered wide area monitoring, protection, and control (WAMPAC) architecture, integrating device-level detection, data aggregation, and an adaptive event classification and validation framework facilitated by dynamic incremental learning (DIL). These expert systems address challenges such as concept drift, catastrophic forgetting, and the need for human-in-the-loop oversight, enabling rapid and accurate identification of both known and novel disturbance events that can be expected in the future

This paper addresses the challenges posed by high Grid-Following (GFL) Photovoltaic (PV) penetration on the dynamic performance of memory-polarized mho relays, crucial for close-in fault protection in power systems. Traditional memory-polarized mho relays, designed for synchronous generator-dominated systems, utilize a scalar weight to dynamically expand their mho characteristics based on memory voltage, enhancing resistive reach. However, the unique transient behavior of Inverter-Based Resources (IBRs) like GFL PV during faults can disrupt this mechanism, compromising relay reliability. To overcome this limitation, this research introduces a novel algorithm that employs a complex weight parameter in the memory polarization process, replacing the conventional scalar approach. This complex weight allows for more precise and adaptable control of the mho characteristic’s dynamic expansion, enabling the relay to better respond to the complex voltage and current transients introduced by GFL PV. The study investigates the dynamic expansion of the mho element’s maximum diameter (dmax) and memory vector angle (θm) under various fault scenarios (three-phase, single-line-to-ground, and line-to-line) to evaluate the algorithm’s effectiveness. The proposed complex weight algorithm is validated across diverse fault types, varying complex weight factors, and different fault resistances, considering GFL PV generators with reactive power priority and IEEE Standard 2800–2022 compliant Low/High Voltage Ride-Through capabilities. The results demonstrate significantly enhanced reliability and stability of memory-polarized mho relays in systems with high GFL PV penetration, showcasing the superior performance of the complex weight approach.

High-frequency resonances in cable-transformer systems can result in excessive overvoltages, increasing the probability of insulation failure for critical components such as power transformers. These resonances occur due to the interaction between the cable and transformer and are influenced by the cable’s characteristics, including the length and wave propagation velocity. In addition, the terminating impedances of the cable’s core and sheath conductors affect the resonance characteristic of the cable as well. Applying single-point sheath grounding to the high-voltage cable connecting the switchgear to the power transformer is conventional. This paper demonstrates that the cable-transformer resonances and resulting overvoltages can vary significantly depending on the end at which the sheath conductors are grounded. An in-depth investigation of such effects is carried out through rigorous mathematical analysis, followed by experimental validation and simulations in an electromagnetic transient (EMT)-based software using models that properly represent the equipment behaviors in a wide-frequency range. The results indicate that sheath conductor grounding configuration can profoundly affect the system response, influencing the severity of transient overvoltages caused by cable-transformer resonances.

Accurate frequency-dependent inductances and resistances are essential for high-frequency transformer models. Traditional analytical approaches, such as cases where eddy-current losses are neglected, or resistances and inductances are computed independently, and numerical techniques such as finite element methods (FEM) are either computationally intensive or rely on simplifications that reduce accuracy. This letter proposes a novel machine learning (ML)-based approach to efficiently estimate these parameters by learning from detailed analytical results. Using a localized feature selection strategy with conductors near the nearest neighbors $k$, the model considers complex electromagnetic interactions while achieving a significant reduction in computation time. This allows for generalization across different winding designs, reducing the dependence on traditional simplifications. Furthermore, the trained ML model achieves high accuracy, with predictions within an error margin of 5% for a wide frequency range. Comparison with measurements confirms the validity and effectiveness of the proposed approach, making it a promising solution for electromagnetic transient simulations.

The increasing penetration of renewable energy sources and frequent lightning and switching events have intensified transient phenomena in modern power systems, exposing power transformers to resonance at critical frequencies. These conditions may cause internal overvoltages, and insulation failure. While many studies focus on wide-band transformer modeling and resonance identification, their primary objective is accurate frequency-domain representation rather than revealing the physical origin of resonance inside the transformer. This paper does not aim to introduce a new transformer modeling method. Instead, it presents a visualization-based approach to identify transformer components responsible for resonance. By analyzing the branch current matrix of a transformer disk model and visualizing current distribution using a color map, dominant resonance-driving elements are identified. This visualization enables protection and future design enhancement.

The growing High-Voltage Direct Current transmission networks require modern control strategies in converter stations to ensure reliable operation and uninterrupted energy supply, particularly under unstable and low Short-Circuit ratio conditions. Conventional Grid Following converters become unstable in low Short-Circuit ratio scenarios, while modern Grid Forming converters, though more robust, exhibit slower dynamic response in high Short-Circuit ratio scenarios. This paper presents a hybrid control switching strategy based on polytopic Lyapunov functions that combines the strengths of Grid Following Control and Grid Forming Control strategies. Switching between these control strategies occurs at defined hyperplanes of polytopes derived from the state-space equations, enabling the system to maintain fast and stable performance under changing Short-Circuit ratio conditions of the grid. Because the method is grounded in polytopic Lyapunov function theory, it demonstrates inherent large-signal stability. This proposed Hybrid Control Strategy is validated using real-time ^ő simulations, showing robust performance during Short-Circuit ratio variations, highlighting its potential for future High-Voltage Direct Current systems.

Ferroresonance, a non-linear and unpredictable disturbance, is rare compared to traditional power system faults occurring in power systems. This rarity, coupled with its complexity, makes it a challenging phenomenon to be detected and identified. This work presents a detection and classification scheme for ferroresonance and its modes. It is carried out by continuously processing the three-phase voltage and current signals using the discrete wavelet transform (DWT). The developed models are simulated in electromagnetic transient software and processed using the DWT to extract fault signatures and predictors. A decision tree classifier is trained to detect and classify a disturbance as ferroresonance using an adaptive time based on the disturbance class. The computational burden of the detection and classification process is significantly reduced by using the superimposed component of the voltage and current to detect transient inceptions before classification. Furthermore, the classification of different modes and classification from other non-linear faults, such as arcing faults, is discussed. The adaptive timing and detection scheme demonstrates that the proposed methodology is efficient and can classify the disturbance into different modes.

Large-scale integration of renewable energy sources (RESs) presents significant challenges for modern power grids, particularly with wind farms playing a crucial role in energy generation. However, frequent switching operations during the (dis)connection of wind farms and lightning strikes on wind turbines can induce severe transient overvoltages. These fast transients (FTs) pose a serious risk to wind farm substations, potentially compromising the reliability of renewable energy generation. In particular, protecting wind farm transformers from FTs, resonances, and resulting overvoltages is essential for ensuring stable operation. This paper proposes a modular series transient suppressor (MSTS) designed to enhance the protection of wind farm transformers against FTs, thereby improving system reliability. The MSTS consists of multiple resonant circuit modules, including a core, a low-voltage capacitor, and a resistor connected in series with the transformer. Its operational behavior is analyzed using analytical methods and validated through simulation studies. Furthermore, experimental testing performed on a developed MSTS prototype confirms its effectiveness in mitigating transient overvoltages for a wind farm transformer model circuit at a 60 kV transient voltage level.

Integration of Inverter-Based Resources (IBRs), like Photovoltaic (PV) and Wind Turbine Generator (WTG), into the power grid necessitates accurate inverter models for assessing their dynamic behaviour and interaction with protection functionalities, such as the Positive Sequence Memory Polarized (PSMP) mho distance relay. This research examines the appropriate Grid Following (GFOL) inverter models for the performance evaluation of PSMP mho relay, considering accuracy and computational efficiency, which is lacking in the existing literature. The research meticulously investigates three different GFOL inverter model structures: detailed, average, and generic models. Each model is thoroughly evaluated for its computational burden and accuracy during events. The GFOL PV generators used in this study are equipped with reactive power priority control and incorporate Low Voltage Ride-Through and High Voltage Ride-Through functionalities. A significant finding of this study is that the generic model can adequately simulate the performance of PSMP mho relays. This offers a valuable solution for computationally efficient system-level studies without compromising relay performance accuracy.

Multiterminal (MT) HVDC grids require dependable DC circuit breaker (DCCB) operation. Electromagnetic-transient (EMT) simulations show that DCCB interruption produces relatively slow-front transient overvoltage (TrOV) whose crest- and duration-stresses on HVDC cable systems are not fully exercised by today’s standard lightning (ULI,std) and switching (USI,std) impulse voltage tests. This paper quantifies these stresses and proposes a parameterized hybrid impulse (Uhyb) superimposed on DC that envelopes SI-class wavefronts and LTrOV-class durations using SI-referenced exceedance metrics (S1, Sn) and t90/t70/t50 checks. A unified type test sequence is outlined, preserving IEC measurement tolerances and existing qualifications, and enabling robust insulation coordination for MT-HVDC grids.

Unscheduled event handling capability and swift recovery from transient events are indispensable study areas to ensure reliability in offshore multiterminal high-voltage dc (MT-HVdc) grids. This article focuses on enhancing the reliability of half-bridge modular multilevel converters (HB-MMCs) in MT-HVdc grids by introducing a predictive dc fault ride-through (DC-FRT) recovery controller and fault separation devices. A novel dc protection-informed zonal DC-FRT scheme for HB-MMCs is proposed, incorporating a model predictive planner for optimized control inputs based on local and interstation measurements and converter constraints. A real-time digital simulator environment simulates the approach, which improves lower level control during fault interruption and suppression by utilizing fault detection and location information. In addition, the study examines two control schemes to assess the impact of communication delays in MT-HVdc grids, a critical factor for system stability and reliability during faults. These schemes include a centralized scheme with delays in input and output signals and a decentralized approach focusing on external signal delays. Both are compared against a baseline centralized control with no delays. These approaches explore alternatives for the placement of the proposed controller, considering potential delays in interstation high-speed communication. The findings underscore the significance of the proposed DC-FRT control in reinforcing MT-HVdc systems against faults, which contributes to efficient recovery and grid stability.

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.

Integrating renewable energy resources such as wind farms is an increasingly prominent trend for future power grids. However, the wind generator tower is consistently at risk of lightning strikes, putting the wind farms’ transformer at risk of damage by lightning transients traveling through the system. Various harmonic contents of the lightning transient can excite the transformer’s resonance frequencies, resulting in both terminal and internal overvoltages (OVs). To effectively safeguard transformers against resonance OVs, it is imperative to first identify the resonance points of the transformer. Following this, a protective method must be implemented to mitigate harmonic content magnitudes that contribute to resonance. This paper introduces a series-protection device comprising an air core reactor and suppressor resistance designed to protect the transformer. The research aims to provide solutions to safeguard wind farm transformers from both terminal and internal resonance OVs caused by lightning transients. The effectiveness of the protection device is assessed through analysis, simulation, and experiments conducted in a high-voltage laboratory setup.

This paper presents a comprehensive model for power transformers, by considering eddy current losses in both the core and conductors. This is achieved through a meticulous analytical approach that ensures high fidelity in representing the transformer's electromagnetic properties. The consideration of magnetic flux effects on inductance and resistance values significantly enhances the model's accuracy and validity. Traditional analytical methods often resort to simplified approaches due to the complexity of these calculations. The paper addresses these limitations by evaluating the eddy current losses in the core and conductors, and by providing a detailed understanding of each component's impact on transformer behavior. Furthermore, by considering the core and conductor effects on the magnetic field distribution, the model handles a wide range of frequencies, making it suitable for conducting comprehensive transient analysis. To validate the model, comparisons with the finite element method and empirical measurements are conducted. Additionally, a reduced-order transformer model is developed using admittance matrix reduction. This approach focuses on the nodes of interest, effectively eliminating not-observed nodes and reducing computational complexity without compromising accuracy. In this way, voltages at specific points of interest are computed efficiently, maintaining the accuracy of the original model.

The increased use of High-Voltage Direct Current transmission networks requires appropriate control strategies for the converter stations, which are crucial to ensure uninterrupted energy supply. In this paper, a Polytopic Lyapunov Function-based Hybrid switching control strategy is implemented to combine the merits of Grid Following and Grid Forming control strategies by switching alternatively from one to another at the polytopes’ hyperplanes to ensure good system response even for faulty conditions. The state space equations of the control strategies are used to form the state hyperplanes for the switching rule. Since the hybrid switching control is based on the Polytopic Lyapunov Function, the system is inherently Large Signal Stable. The results obtained by real-time-based simulations using RTDS verify the designed control for various transient phenomena.