Fault Detection and Classification in Hybrid Shipboard Microgrids

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.

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.

In this paper fault detection and classification scheme using K nearest neighbor (KNN) has been presented for AC/DC transmission line with DFIG (doubly fed induction generator). The power system model consist of 1000 MW synchronous generator, 510 MW wind far, three phase transmission line of 500 kV and 30km length and HVDC line of 1000 MW. The model has been implemented with the corresponding parameters in MATLAB and simulated for different scenario. The raw current and voltage acquired for the sensors have been processed by performing discrete wavelet transform to the derive the discriminatory attributes with maximum disparity between the healthy and faulty cases. Following feature extraction, the required tasks of fault detection and classification scheme in A C and HVDC lines have been achieved using KNN. Further, the efficacy of the proposed KNN based scheme has been validated for varying fault scenarios pertaining to wide variation in fault resistance, inception angle of fault and fault location. For all tested cases, the proposed scheme is able to achieve a fault detection and classification accuracy of 1 00%.

In this article, a new full-scheme distance protection for a series-compensated transmission line is proposed. The new combination of hyperbolic S-transform and learning machines is applied for fault detection, classification, and location, which are the three main aspects of distance relays. The hyperbolic S-transform is used for extracting useful features from the current and voltage signals sample of the power system from one terminal. The extracted features are employed for distance protection using support vector classification and support vector regression methods. The proposed method is evaluated by the different series capacitor location in the transmission line, where a metal oxide varistor protects the series capacitor against over-voltages. Extensive simulation experiments show that the proposed protection scheme provides accurate tripping logic with good accuracy of fault location. Also, the non-tripping decision for external faults that occurred on adjacent uncompensated transmission line is embedded in the proposed scheme. The various operating conditions of the series-compensated transmission line are simulated in the PSCAD/EMTDC (Winnipeg, Manitoba, Canada) environment. The obtained results of support vector classification and support vector regression confirm that the new extracted features from the time-frequency hyperbolic S-transform-matrix are very effective and reliable.

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 paper describes the fault classification and detection of SVC (Static VAR Compensator) integrated differential relay based double circuit transmission line applying Discrete Wavelet Transform (DWT) approach. The SVC is placed at mid-point of circuit-I of double transmission line. The other circuit (circuit-II) remains uncompensated. The differential relay based protection scheme is used for the analysis of fault detection and classification. The process starts by extracting current signals (A, B or C-phase) from both sending and receiving end of the transmission line synchronously and processes through DWT to obtain the spectral energy (SE) at both the end of the line. The Differential Spectral Energy (DSE) of each phase current signals are obtained by SE of current measured at sending end minus SE of current measured at receiving end. This DSE value is used to register the fault pattern.

Fault location and distance protection in transmission lines are essential smart grid technologies ensuring reliability of the power system and achieve the continuity of service. The objective of this chapter is to presents an accurate algorithm for estimating fault location in Extra High Voltage (EHV) transmission lines using Artificial Neural Networks (ANNs) for high speed protection. The development of this algorithm is based on disturbed transmission line models. The proposed fault protection (fault detection/classification and location) uses only the three phase currents signals at the one end of the line. The proposed technique uses five ANNs networks and consists of two steps, including fault detection/classification and fault location. For fault detection/classification, one ANN network is used in order to identify the fault type; the fault detection/classification procedure uses the fundamental components of pre-fault and post-fault sequence samples of three phase currents and zero sequence current. For fault location, four ANNs networks are used in order to estimate the exact fault location in transmission line. Magnitudes of pre-fault and post-fault of three phase currents are used. The ANNs are trained with data under a wide variety of fault conditions and used for the fault classification and fault location on the transmission line. The proposed fault detection/classification and location approaches are tested under different fault conditions such as different fault locations, different fault resistances and different fault inception angles via digital simulation using MATLAB software in order to verify the performances of the proposed methods. The ANN-based fault classifier and locator gives high accuracy for all tests under different fault conditions. The simulations results show that the proposed scheme based on ANNs can be used for on-line fault protection in transmission line.

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.

Transmission lines are crucial for efficient and reliable electricity delivery of electricity but can also be susceptible to faults due to various factors such as external interference, device failures, and ambient conditions. The reliability, accuracy, and efficiency of fault management on the power grid depend heavily on fault classification and fault location. This study integrates a fault detection and classification system for transmission lines and an automatic fault localisation system (ANFIS) for fault location to present a comprehensive approach to managing transmission line problems. The proposed methodology aims to address the technological challenges associated with fault management in power grids by leveraging existing research on radial basis function neural networks (RBFNN) and ANFIS. The integration and validation of the proposed methodology involve incorporating improved fault classification via RBFNN and robust fault locating through ANIS. Simulations and real-world testing will validate the integrated methodology, assessing its performance in various fault scenarios and system configurations. The WSCC 9-bus system is used to validate and test the proposed design, which includes power plants, transformers, transmission networks, and distribution networks. The ClasLoc system has a classification accuracy of 90% and a location accuracy of 89%, ensuring a safe working environment.

<p>This paper introduces a novel hybrid approach that integrates Latin hypercube sampling (LHS) and Bayesian optimization for optimizing artificial neural networks (ANNs) in fault detection, classification, and location for transmission lines. The proposed method advances the accuracy and efficiency of fault diagnosis in power systems, representing a significant step forward compared to conventional approaches. The test system is a 400 kV, 50 Hz, 300 km transmission system, and the simulations were carried out in MATLAB/Simulink environment. Using the strategic insight of LHS, optimal initial points were determined, which subsequently formed the basis for the Bayesian optimization to further refine the learning rate and training epochs. Within the fault detection domain, the model showcased remarkable precision when deployed on an evaluation dataset of 168 cases, accurately detecting every instance of normal and faulty scenarios. This culminated in an astounding 100% accuracy in fault detection. In terms of fault classification, the ANN model, trained on a dataset of 495 instances, revealed perfect regression coefficients across the training, testing, and validation subsets. When tested against unseen data, it demonstrated its proficiency by correctly classifying 154 out of 154 cases, showcasing a 100% F1 score. Also, the accuracy figures in terms of fault location fluctuated between 99.826% and 99.999%, with a mean absolute percentage error (MAPE) of 0.053%. The model's mean square error (MSE) stood at 0.0083, while the mean absolute error (MAE) was calculated at 0.0717. A deep dive into diverse fault types reaffirmed the model's precision, underscoring its consistent performance across various fault scenarios. The trained models were deployed on three different transmission lines and the models exhibited remarkable precision in all the cases tested. In summary, the innovative hybrid optimized ANN model, weaving together the strengths of LHS and Bayesian optimization, signifies an advancement in the field of power system fault analysis, ushering in heightened efficiency and reliability.</p>

Faults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.

Modern day protection systems require easy implementation of detection and classification algorithms. Analysis of three phase voltages can impart substantial information regarding the detection and type of the faults. These signals vary with several factors like fault location, inception angle and impedance. In this study, the detection and classification of high impedance faults in a transmission line is proposed using alienation coefficients which are obtained by juxtaposing half cycles of absolute voltage signals using the moving window technique. The alienation coefficients obtained are compared with the corresponding threshold values to detect and classify the faults. Case studies undertaken by the variation of fault locations and fault inception angles have successfully validated the proposed algorithm.

This paper presents application of ANFIS and ANN in fault classification and location in a long transmission line. Compared to other methods, Machine Learning techniques based on artificial intelligence perform the best in fault classification and finding its location. Most frequently used ML techniques for this purpose are ANFIS and ANN. Both the techniques were able not only to identify fault type but also to find the fault location in the transmission line very accurately using source end current and voltage data. Common training and testing data was used for ANFIS and ANN. This data was obtained from simulation of faults in a long transmission line model using MATLAB. Error analysis and comparison of both the techniques is also presented in this paper. A GUI was designed for comparison of both the methods.

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.

The advanced application of artificial intelligent techniques (AIT) was introduced recently in protection of transmission lines in electric power systems (EPS). Adaptive neuro-fuzzy inference system (ANFIS) is a promising tool among these AIT. In this proposed research, the application of ANFIS for distance relay protection for long transmission line, under different loading conditions, in electrical power systems (EPS) will be introduced and discussed. Based on multiple classified ANFIS and considering different loading conditions for long transmission lines in EPS, the suggested and proposed technique deals with fault detection, classification, and location in long transmission lines. All these tasks will be addressed in details in this article. It considers, firstly, detecting the fault occurrence in very short time and isolate the faulty section of the long transmission lines. Secondly to classify the fault type and deduce which of the three phases are exposed to the fault. Finally, locating the fault will be achieved easily even if the procedure here is completely different from short and medium transmission lines. The input training data of the ANFIS detection units are firstly derived from the fundamental values of the voltage and current measurements [using digital signal processing via discrete Fourier transform (DFT)]. These measurements were simulated considering different loading conditions.