In this study, a novel algorithm for detecting and classifying faults in transmission lines is proposed. The algorithm is based on mathematical morphology and initial current travelling waves. A new morphological edge detection (MED) filter to extract the transient features from the original fault signal is designed. This MED filter can fast and accurately detect the arrival time and polarity of travelling waves in all conditions. The appropriate criteria of fault classification and faulted‐phase selection are introduced based on polarity of initial current travelling waves. The simulations based on the electromagnetic transients program and MATLAB have been done to evaluate the validity of the proposed algorithm.

Here, a new travelling wave‐based fault location method is proposed for branched networks. A mathematical morphology‐based filter is used to analyse fault‐induced transient signals and detects the arrival time of travelling waves. In the proposed algorithm, using the first surge arrival time of travelling waves at each of the travelling wave detectors installed in the network, a set of linear equations is constructed. By solving the equations, the probable fault occurrence points on the main line of the network are determined. Finally, proper evaluation criterions are introduced to determine accurate faulted lateral branch or fault location on the main lines. Extensive simulation studies using EMTP and MATLAB are carried out to examine the effectiveness of the proposed method. The obtained results verify the high accuracy, noise immunity, and fault impedance robustness of the proposed method.

Despite the importance of high‐impedance fault (HIF) modelling as a key issue in numerous power system studies, there is no single model accurately representing the behaviour of each type of HIFs. Tree‐related HIF (THIF) is a complex type that occurs when a power line comes in contact with a live tree. Given that available models for HIF cannot represent the expected behaviour of a live THIF, the need for a more accurate model seems obvious. So, in this study, based on experimental data obtained from measurements, the effective factors in HIF caused by live trees are studied and a new mathematical model of THIF provided in order to present the effects of both environmental conditions and biological classification. This model represents low and high frequency behaviours of THIFs, which are, respectively, derived in block‐oriented feed‐forward form and sinusoidal functions. Moreover, this study proposes a hybrid technique based on the combination of kernel density estimation and wavelet analysis to perform a feature extraction from THIF signals.

Fault location in DC microgrids (DCMGs) is a critical challenge due to the system’s inherent complexities and the demand for high reliability in modern power systems. This study proposes an explainable artificial intelligence (XAI)-based quantum deep neural network (QDNN) framework to address fault localization challenges in DCMGs. First, voltage signals from the DCMG are collected and analyzed using high-order synchrosqueezing transform to detect traveling waves (TWs) and extract critical fault parameters such as time of arrival, magnitude, and polarity of the first and second TWs. These features are fed into the proposed QDNN model that integrates advanced learning techniques for accurate fault localization. The cumulative distance from the fault point to the bus connecting the DCMG to the power network is considered the output vector. The model uses a combination of deep learning and quantum computing techniques to extract features and improve accuracy. To ensure transparency, an XAI technique called Shapley additive explanations (SHAP) is applied, enabling system operators to identify critical fault features. The SHAP-based explainability framework plays a critical role in translating the model’s predictions into actionable insights, ensuring that the proposed solution is not only accurate but also practically implementable in real-world scenarios. The results demonstrate the QDNN framework’s superior accuracy in fault localization even in noisy environments and with high-resistance faults, independent of voltage levels and DCMG configurations, making it a robust solution for modern power systems.

This study aims at proposing an original single‐end protection scheme for the compensated transmission line using traveling waves and game theory. To this end, this paper initially investigates the effects of static synchronous compensator (STATCOM) on traveling waves passing through STATCOM installation point. Furthermore, based on the traveling wave theory, it evaluates the impacts of STATCOM's harmonic on conventional protections. A single‐end method will be proposed in the next stage for fault protection on the compensated transmission lines with STATCOM. The extracted traveling waves in this study will be discriminated using the game theory, and discrete wavelet transform will be utilized to process the signals and assess the sensitivity of the proposed scheme considering the extraction techniques of the traveling wave. According to the obtained results, the proposed protection scheme is a reliable and accurate method for all possible faults type on compensated transmission lines at the various locations of the line and with different fault inception angles.