A novel dc fault protection scheme based on intelligent network for meshed dc grids

Yousaf, Muhammad Zain; Khalid, Saqib; Tahir, Muhammad Faizan; Tzes, Anthony; Raza, Ali

doi:10.1016/j.ijepes.2023.109423

Cited by 20 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison to the existing benchmark schemes including Mathematical morphology-based scheme [9], Pseudo-datadriven-based scheme [22], ANN-based scheme [29], and Entropy-based scheme [18]. It is shown in TABLE 4 that the proposed scheme demonstrates significant improvements in accuracy, computational burden, and operation time.…”

Section: Comparative Analysismentioning

confidence: 99%

Enhanced Fault Detection and Localization Strategy for High-Speed Protection in Medium-Voltage DC Distribution Networks Using Extended Kalman Filtering Algorithm

Larik,

Li,

2024

IEEE Access

View full text Add to dashboard Cite

Conventional strategies are not effective in addressing the complex protection challenges in medium-voltage DC distribution networks (MVDCDN). The main challenge in MVDCDN is the high-rising DC fault current, requiring a robust and fast protection strategy. This paper proposes the use of an Extended Kalman filter (EKF) to detect various types of DC faults using only the current signal in the MVDCDN. In the first stage, current signals from the positive and negative poles corresponding bus are obtained. The EKF is then applied to the measured DC-current signals to generate two fault detection indices. The first index is the cumulative residuals (CR), calculated using the EKF iterative differencing process with updated current estimated state and noisy measurement. The second index is the modified DC version of total harmonic distortion, known as DC distortion factor (DCDF). The fault classification/zone identification (FCZI) unit is activated if changes in CR and DCDF are detected within the observation window of the relay. In the second stage, the FCZI unit calculates the Extended Kalman filter-based predicted energy (EKFBPE) for the faulty DC line section at both ends. The polarity of EKFBPE is used for fault classification and localization decisions. The proposed protection strategy requires low-band wireless communication capability in the smart grid. Extensive simulations using MATLAB ®Simulink 2022b are conducted on a ±2.5 kV MVDCDN with three feeders, considering various fault scenarios. The results demonstrate that the proposed scheme achieves 99.9% accuracy, under radial, looped, and meshed topology and is highly resilient to different types of faults with time of operation 1 msec. The scalability of proposed method and its effectiveness in handling higher voltage levels and associated fault uncertainty will investigate in future research.

show abstract

Section: Comparative Analysismentioning

confidence: 99%

Enhanced Fault Detection and Localization Strategy for High-Speed Protection in Medium-Voltage DC Distribution Networks Using Extended Kalman Filtering Algorithm

Larik,

Li,

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, it also signi cantly increases the number of hyperparameters 28 . These hyperparameters, including the number of nodes per layer, the initial learning rate, and the number of iterations, are pre-set before model training begins 29 . Assessing each combination of hyperparameters necessitates numerous iterative computations, leading to signi cant time and effort expenditure.…”

Section: Introductionmentioning

confidence: 99%

Wear fault diagnosis in hydro-turbine via the incorporation of the IWSO algorithm optimized CNN-LSTM neural network

Dao,

Zeng,

Zou

et al. 2024

Preprint

View full text Add to dashboard Cite

Diagnosing hydro-turbine wear fault is crucial for the safe and stable operation of hydropower units. A hydro-turbine wear fault diagnosis method based on improved WT (wavelet threshold algorithm) preprocessing combined with IWSO (improved white shark optimizer) optimized CNN-LSTM (convolutional neural network-long-short term memory) is proposed. The improved WT algorithm is utilized for denoising the preprocessing of the original signals. The CNN-LSTM hydro-turbine wear fault diagnosis model is constructed. Aiming at the problem that the WSO algorithm quickly falls into local optimum and premature convergence, tent chaotic mapping is used to initialize the population and birds flock search behavior. The cosine elite variation strategy is introduced to improve convergence speed and accuracy. Hyperparameter tuning of CNN-LSTM model based on IWSO algorithm. The experimental results show that the accuracy of the proposed method reaches 96.2%, which is 8.9% higher than that of the IWSO-CNN-LSTM model without denoising. The study also found that the diagnostic accuracy of hydro-turbine wear faults increased with increasing sediment concentration in the water. This study can supplement the existing hydro-turbine condition monitoring and fault diagnosis system. Meanwhile, diagnosing wear faults in hydro-turbines can improve power generation efficiency and quality and minimize resource consumption.

show abstract

A novel deep learning approach for estimating and classifying short-term voltage stability events in modern power systems with composite load and distributed energy resources

Vineeth,

Vijayalakshmi

2024

Electr Eng

View full text Add to dashboard Cite

A novel dc fault protection scheme based on intelligent network for meshed dc grids

Cited by 20 publications

References 36 publications

Enhanced Fault Detection and Localization Strategy for High-Speed Protection in Medium-Voltage DC Distribution Networks Using Extended Kalman Filtering Algorithm

Enhanced Fault Detection and Localization Strategy for High-Speed Protection in Medium-Voltage DC Distribution Networks Using Extended Kalman Filtering Algorithm

Wear fault diagnosis in hydro-turbine via the incorporation of the IWSO algorithm optimized CNN-LSTM neural network

A novel deep learning approach for estimating and classifying short-term voltage stability events in modern power systems with composite load and distributed energy resources

Contact Info

Product

Resources

About