Online Fault Detection, Classification and Localization in PV Arrays Using Feedforward Neural Networks and Residual-Based Modeling

Fault detection and classification in electrical motors are essential for condition monitoring and predictive maintenance. This study proposes a hybrid learning framework that combines genetic algorithm–optimized Morse wavelet–based time–frequency analysis with machine learning to investigate their effectiveness in diagnosing single and simultaneous combined faults in electrical motors under different load and speed conditions. One-dimensional signals are segmented using a centrally expanding sliding window strategy and transformed into two-dimensional representations through a Morse-based continuous wavelet transform, where spectral concentration metrics are used to guide fault-specific parameter optimization. Deep features are extracted using a pre-trained ResNet50 backbone, and fault classification is performed using a support vector machine. The proposed framework is initially evaluated on the Case Western Reserve University dataset using ten-fold cross-validation. The results indicate reliable diagnostic behavior under clean conditions and maintain moderate performance in the presence of additive white Gaussian noise. To further examine generalization capability, the proposed approach is evaluated on a custom experimental motor acoustic dataset covering single and combined fault scenarios under varying operating conditions. The observations suggest that feature-based learning strategies can provide a practical balance between representational capacity and robustness when operating conditions differ between training and evaluation. Comparative analyses further indicate that selective fine-tuning of a limited portion of a deep backbone, combined with feature-based classification, offers a more stable and adaptable alternative to fully end-to-end training in data-constrained and cross-domain scenarios.

The integration of DC networks including DC microgrids (DCMGs) into power systems is rapidly increasing. This is notably attributed to the distinctive fault current characteristics arising from inverter-based distributed generation resources in DCMGs, which differentiate them from conventional networks. As a result, the protection of DCMGs presents considerable challenges. Leveraging the recent strides in artificial intelligence, this paper introduces a novel multi-agent-based protection scheme for DC microgrids. Subsequently, three fault classification approaches are proposed in an intelligent protection scheme platform, employing diverse machine learning-based methods as a backup protection for fault detection and the main protection for fault location. The proposed protection scheme uses three main protection layers—namely, equipment, substation, and system—each endowed with specialized agents. In this way, the first and second fault classification approaches employ classifiers based on machine learning algorithms, such as Support Vector Machine (SVM) and Decision Tree (DT), to ascertain the microgrid status. Subsequent fault location is accomplished through various neural networks dedicated to the fault location. In the third approach, three Deep Neural Networks (DNNs) are proposed for fault classification, prompting the exclusion of classifiers from the substation layer due to the heightened training proficiency of DNNs. Intelligent Electronic Devices (IEDs) are placed at the beginning of the lines and send voltage and current information to the substation layer. Communication among agents and layers is performed by the IEC-61850 protocol. Comprehensive simulations and analyses are conducted using DIgSILENT, MATLAB, and Python (TensorFlow platform and Keras library) software. The findings underscore the efficacy of the proposed scheme and Fault Detection and Location (FDL) approaches, affirming their capability for precise fault classification and location determination.

This study aims to improve the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy system. Three separate fuzzy inference system are designed for complete protection scheme for transmission line. The proposed technique is able to accurately detect the fault (both forward and reverse), locate and also identify the faulty phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault inception angle, fault resistances and fault location. The proposed method needs current and voltage measurements available at the relay location and can perform the fault detection and classification in about a half‐cycle time. The proposed fuzzy logic based relay has less computation complexity and is better than other AI based methods such as artificial neural network, support vector machine, and decision tree (DT) etc. which require training. The percentage error in fault location is within 1 km for most of the cases. Fault location scheme has been validated using χ2 test with 5% level of significance. Proposed scheme is a setting free method and is suitable for wide range of parameters, fault detection time is less than half cycle and relay does not show any reaching mal‐operation so it is reliable, accurate and secure.

ABSTRACTFaults in power transmission systems pose significant challenges due to the complexity and length of transmission lines. Effective fault detection, classification and location are essential for preventing further damage to the power grid. While travelling wave‐based algorithms are commonly used for fault location, they often focus on identifying the fault's location without classifying the fault type. Accurate classification is crucial for enabling efficient and timely responses from protection systems. This paper introduces an integrated model for fault detection, classification and location using voltage signals from a single terminal of a series‐compensated transmission line with a static synchronous series compensator (SSSC). The Gabor Transform (GT) is utilised for feature extraction, enabling both fault detection and classification. Travelling wave theory is then applied to identify the faulty segment and estimate the fault location. Additionally, a novel technique adaptively calculates the threshold value during the protection algorithm's execution. The proposed method is validated through a comprehensive analysis of various fault scenarios and sensitivity analysis. Numerical simulations in MATLAB/Simulink show that the model achieves 100% accuracy for fault detection, classification, and faulty segment identification, with 99.7925% accuracy for fault location estimation, demonstrating its effectiveness in fault management.

Distance protection has been a challenging task due to the complexity of the power system. An effective protection scheme must perform fault detection, classification and location that help in quick restoration of the power supply. Although significant hybrid methods are available for fault detection, classification, and location, separately; there is always a need for an improved integrated approach performing fault detection, classification, and location. This chapter presents an integrated approach using autoregressive modeling and extreme learning machine (ELM). The performance of the scheme is investigated on 11 kV, 6 km medium voltage underground cable. The test system is simulated using PSCAD/EMTDC and the integrated protection scheme is implemented in MATLAB. The scheme uses half-cycle duration current samples. The data preprocessing is done using autoregressive (AR) modeling. The scheme detects the fault using the power of the critical pole. ELM binary classifier uses the AR coefficients to classify the type of fault. ELM in regression mode locates the fault distance. A total of 1260 data set is used to train the ELM fault classifier and ELM fault locator. The integrated protection scheme is tested with 10,800 test cases covering wide variation in operating conditions. The classification accuracy of the ELM fault classifier is 99.85% and the fault location error is less than 0.12% for the ELM fault locator, which is better than the other hybrid methods. Hence this integrated approach can be considered as quite suitable for protection of medium voltage underground cables.

Discriminative common vector in sufficient data Case: A fault detection and classification application on photovoltaic arrays

Kernel approaches for fault detection and classification in PARR-2

Deep spatiotemporal feature fusion using CNN-LSTM autoencoder for real-time, phase-aware anomaly identification and localization in high-voltage power transmission networks

Switching LDS-based approach for process fault detection and classification

Fault detection, classification and location from the relay end in a double circuit transmission line are challenging tasks because of mutual coupling between the two circuits. In this paper, combined unsupervised and supervised neural networks-based fault detection, classification and distance location techniques are presented for a double circuit line. This technique does not require communication link to retrieve the remote end data. Zero sequence current compensation for healthy phases can also be avoided. Artificial neural network employs a reduced set of input features, i.e., the fundamental components of three phase voltages and the six phase currents of the two parallel lines at one end of the line only. The simulation results show that single phase-to-ground faults can be correctly classified and located after one cycle from the inception of fault. The complexity of the possible types of faults, fault locations, high path fault resistances, fault inception angles, mutual coupling effects and remote end in-feed do not affect the performance.

The artificial neural network is a powerful tool for the detection of the transmission line faults due to its ability to differentiate between various patterns. This paper deals with the application of artificial neural networks (ANNs) to the fault detection and classification in high voltage transmission lines for high speed protection which can be used in digital power system protection. The three phase currents of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. The ANN was trained and tested using various sets of field data, which was obtained from the simulation of faults at various fault scenarios (fault types, fault locations and fault resistance) of 230 kV, 193.2 km in length “Mansan-Shwesaryan, Mandalay Region, Myanmar” transmission line using a computer program based on MATLAB/Simulink. Simulation results confirm that the proposed method can efficiently be used for accurate fault classification on the transmission line.

This study aims to develop a real-time model for detecting and classifying various fault types in power distribution networks to enhance reliability and operational efficiency. A hybrid DWT–SVM approach is essential for accurately detecting and classifying faults in modern power distribution systems, especially under non-stationary and dynamic conditions. The proposed method addresses a range of fault scenarios, including single-phase-to-ground, line-to-line, double-line-to-ground, and three-phase faults, within a 33 kV distribution network. A hybrid approach combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) is introduced. Using the Debauchies-4 (Db4) wavelet, DWT effectively decomposes transient fault currents at the source terminal, capturing critical time-frequency domain features. Fault classification is performed using an SVM optimized with a Radial Basis Function (RBF) kernel and Particle Swarm Optimization (PSO), enabling precise mapping of data into higher-dimensional spaces for optimal separation. Validation conducted with MATLAB R2023b demonstrates a detection accuracy of 100% and a classification accuracy of 99%. Comparative analyses against models such as PSO-based Support Vector Machine (PSO-SVM), DWT-DNN and wavelet transform with artificial neural networks (WT-ANN) on the IEEE 13-bus system highlight the proposed method's superior performance. This innovative approach proves to be robust, adaptable, and highly effective across diverse fault scenarios, offering significant improvements in accuracy and reliability for fault detection and classification in power distribution networks. The method supports real-time operation by using high-speed simulation platforms MATLAB/Simulink to process signals and classify faults within milliseconds using a pre-trained SVM model.

A scheme for detection, classification and location of transmission line faults, which combines the wavelet multi-resolution analysis approach and the differential equation approach, is presented. While detection and classification of faults is carried out by a wavelet analysis based algorithm, fault location is determined by a differential equation based algorithm. Using EMTP and MATLAB, studies have been carried out on two simulated power system models. The models are subjected to different types of faults while operating at different operating conditions and the performance of the proposed scheme is evaluated. The results of the simulation studies, which are presented, confirm the feasibility of the proposed scheme.

Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements.

Recently, fault detection has played a crucial role in ensuring the safety and reliability of inverter operation. Switch failures are primarily classified into Open-Circuit (OC) and short-circuit faults. While OC failures have limited negative impacts, prolonged system operation under such conditions may lead to further malfunctions. This paper demonstrates the effectiveness of employing Artificial Intelligence (AI) approaches for detecting single OC faults in a Packed E-Cell (PEC) inverter. Two promising strategies are considered: Random Forest Decision Tree (RFDT) and Feed-Forward Neural Network (FFNN). A comprehensive literature review of various fault detection approaches is first conducted. The PEC inverter’s modulation scheme and the significance of OC fault detection are highlighted. Next, the proposed methodology is introduced, followed by an evaluation based on five performance metrics, including an in-depth comparative analysis. This paper focuses on improving the robustness of fault detection strategies in PEC inverters using MATLAB/Simulink software. Simulation results show that the RFDT classifier achieved the highest accuracy of 93%, the lowest log loss value of 0.56, the highest number of correctly predicted estimations among the total samples, and nearly perfect ROC and PR curves, demonstrating exceptionally high discriminative ability across all fault categories.