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.

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.

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.

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.

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.

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.

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.

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.

Deteriorated measurements due to the saturation of current transformers (CTs) are a major challenge in digital power system protection schemes. Failing to resolve this issue effectively can have serious consequences for the accuracy of measured components in phasor-based digital relaying algorithms. Potentially, this may compromise the secure and reliable operation of the protection system and, hence, the entire power system. Accurate fault detection or classification can be achieved by applying an algorithm that can deal with the CT saturation effects. In this paper, a method is presented that can accurately estimate the fundamental current phasor during CT saturation. Reconstruction of the deteriorated measured waveform is accomplished by applying a supervised machine learning algorithm, namely the support vector machine (SVM). The least squares (LS) method is integrated with the SVM-based algorithm to reduce the complexities of waveform reconstruction regressions. A modified discrete Fourier transform (DFT), robust to decaying DC components, is then applied to the reconstructed waveform to extract the required phasor components. The proposed approach is validated by evaluating its classification and phasor estimation performance using standard metrics over numerous simulated cases and field measurements. The results demonstrate the high accuracy of the proposed method to classify different levels of CT saturation and ensure precise estimation of the fundamental phasor component.

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.

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.

GigaWatt-scale offshore wind farms are being connected using the Modular Multilevel Converter (MMC), in which submodules act as the main building block. The extensive number of connections has a significant effect on the system's security of supply. Periodic weather conditions and time-dependent labor capacity complicate maintenance, emphasizing the need for more flexibility when replacements are required. Traditionally, submodules are redundant or over-dimensioned to increase reliability, but do not resolve flexibility issues faced by operators. This article presents a novel operation method introducing flexibility in the submodule stress distribution. Specific submodules can be used more or less frequently depending on maintenance achievability. The technical condition of each submodule is determined, and then a selection window with a configurable length determines the submodule-specific insertion frequency, affecting the remaining useful lifetime. The capacitor voltage balance can be guaranteed with traditional sorting methods and is compatible with the lifetime optimization algorithm using a priority factor. The priority can be divided between lifetime optimization and capacitor voltage balancing depending on the operator's needs. This is superior to the traditional methods due to the ability to control the submodule-specific deterioration pace. Analytical evaluation and simulation studies demonstrate the effectiveness of this control approach.

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 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.

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.

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.

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.