Aug 2026· International Conference on Industrial IoT, Big Data, and Smart Cities· Vol 14325, pp. 143251J - 143251J-10· 0 citations· 9 references
Engineering
TL;DR
A neural network-based multi-parameter fault type identification strategy that exhibits strong robustness against variations in fault location, transition resistance, and source type, providing a reliable and adaptive protection solution for evolving hybrid power grids.
Abstract
With the large-scale integration of inverter-based resources (IBRs), the types and operating characteristics of power sources on both sides of transmission lines have changed significantly, rendering traditional fault-type selection methods inadequate. To address this, this paper proposes a neural network-based multi-parameter fault type identification strategy. The method constructs a 16-dimensional feature vector from local three-phase voltage/current magnitudes, phase angles, and zero-sequence components, and designs a lightweight fully-connected neural network with two hidden layers to learn the complex nonlinear mapping between these comprehensive inputs and fault types. Extensive training and testing data are generated using the PSCAD/EMTDC simulation platform, covering multiple scenarios including double-ended synchronous generator (SG), single-ended IBR, and double-ended IBR. The results show that the proposed strategy achieves identification accuracy exceeding 95% across all scenarios, significantly outperforming traditional current-based methods, especially in IBR-dominated cases. Moreover, the method exhibits strong robustness against variations in fault location, transition resistance, and source type, providing a reliable and adaptive protection solution for evolving hybrid power grids.
The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes, whereas conventional voltage and current measurements alone represent a simpler and more stable alternative, whereas phase-angle measurements may be incorporated when synchronized phasor information is already available.
Zeynep Bala Duranay, İsmail Anıl Avcı, Mohammed Bushra Mohammed et al.· Symmetry· 0 citations
- Transmission-line protection in modern power networks faces growing challenges from high-impedance faults, current-transformer saturation and power swings that degrade the performance of settings-based conventional distance relays. This paper reports a comparative simulation study of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)-derived intelligent protection techniques, benchmarked against Deep Learning (DL) and Genetic-Algorithm (GA) optimized variants, for fault detection, classification and mitigation on the 330 kV Onitsha – Enugu transmission corridor in Nigeria. A distributed-parameter, twenty-section π -model of the 250 km line, coupled with a ±150 MVAr Static Synchronous Compensator (STATCOM) under fuzzy-logic control, was developed in MATLAB/Simulink. Line-to-ground, line-to-line, double line-to-ground and three-phase faults were simulated at varying fault resistances, inception angles and locations to build a training and test dataset. A Multi-Layer Feed-Forward ANN trained with the Levenberg – Marquardt algorithm achieved 96.0% fault-classification accuracy with a fault-location error below 2.0%, while GA optimization raised classification accuracy to 97.2%. The fuzzy-logic-controlled STATCOM reduced voltage-recovery time from 0.25 s to 0.1 s (a 60% improvement) and limited the peak fault-current surge by 74%. The results confirm that ANN-and ANFIS-derived architectures markedly outperform conventional threshold-based protection and reveal an accuracy – interpretability trade-off that motivates hybrid intelligent-relay deployment.
Nwoye Bernard Amobi, U. Anionovo, Abigail Chidimma Odigbo et al.· Iconic research and engineer...· 0 citations
Low voltage direct current (LVDC) microgrids are increasingly adopted due to their efficiency in integrating distributed energy resources (DERs) and DC loads without multiple conversion stages. However, the presence of high amplitude fault currents and the vulnerability of electronic devices pose significant challenges to reliable protection. Conventional electromechanical switches suffer from slow response times, making them unsuitable for modern LVDC systems. Solid-state circuit breakers (SSCBs), with their millisecond-level interruption capability, offer a promising alternative. This paper introduces an artificial neural network (ANN)-based protection strategy integrated with a bi-directional SSCB for DC bus fault mitigation. The ANN is designed to optimize fault detection and clearing time, achieving rapid isolation within 0.25 ms. A feedforward two-layer neural network with 20 hidden neurons and a sigmoid activation function is trained using multiple algorithms, including Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. Simulation results demonstrate superior performance compared to conventional differential protection, with the LM algorithm providing the most accurate and efficient fault clearing. The proposed ANN-SSCB approach enhances system reliability, equipment safety, and overall resilience of LVDC microgrids.
Eswaraiah Giddalur, Askani Jaya Laxmi· Indonesian Journal of Electr...· 0 citations
The proposed framework employs ANN as a nonlinear feature embedding and a Radial Basis Function SVM subsequently classifies using an Error-Correcting Output Codes (ECOC) strategy, validating the effectiveness of the proposed hybridisation strategy for intelligent transmission system protection.
Kudu Abubakar Mohammed, M. Balogun, Adesina M. Lambe et al.· Communication in Physical Sc...· 0 citations
Large-scale renewables are weak-feed and their output current is controlled. That doesn't play nice with traditional pilot protection, which only looks at power-frequency components. When a fault happens, the current from these sources has a "low fundamental, high transient" signature—so the protection often ends up tripping when it shouldn't. This paper proposes a novel adaptive protection scheme utilizing a hierarchical weighted Euclidean distance. It constructs a duallayer feature vector from low-frequency (50Hz) and high-frequency (1kHz) current components measured at both line ends. A composite fault indicator is calculated by adaptively weighting the Euclidean distances within each layer based on real-time signal-to-noise ratio and line attenuation. This approach leverages complementary fault information across frequency bands. Simulation results in PSCAD/EMTDC demonstrate that the proposed method significantly outperforms traditional differential protection in sensitivity and reliability, effectively mitigating maloperation risks under highimpedance faults and weak-infeed conditions, offering a viable software upgrade path for existing infrastructures.
Ying Zou, Luyun Zhang, Chenyang Wang et al.· International Conference on...· 0 citations
A robust Machine Learning (ML)-based framework for accurately locating electrical faults in wind farm collector networks and achieves the highest accuracy, with prediction errors not exceeding 2%.
Miguel R. Fonseca, M. Davi, M. Oleskovicz· IEEE Access· 0 citations
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