Hilbert–Huang Transform Based Transient Analysis in Voltage Source Converter Interfaced Direct Current System

Fault detection method using high-pass filtering in VSC based multi-terminal DC system

The rapid discharge of a dc-link capacitor of the voltage-source converter (VSC)-based dc system is the primary indication of the fault condition. Apart from the time-domain analysis, frequency-domain analysis of the fault current could also be utilized for dc fault detection, as the rapidly rising fault current is expected to have high-frequency components. This paper proposes two fault detection methods and compares their performances with the wavelet transform. The first method is the time-domain analysis of the dc-link capacitor discharge and is termed as the capacitive discharge technique. The relationship between the dc line current and the behavior of the dc-link capacitor is measured in terms of a correlation coefficient, whose value can be used to establish a fault basis. The second method is the frequency-domain-based short-time Fourier transform, which is used for quantitative analysis of high-frequency components in the fault current. These methods are extensively analyzed and compared using a scaled-down VSC-based dc system experimental test setup. Comparison has been done based on fault detection time, sensitivity to fault parameters, influence of sampling frequency, and computation speed. Furthermore, the selectivity of the fault detection methods is studied on the multiterminal dc systems of two different topologies (ring and radial), modeled in PSCAD/EMTDC. The experimental and simulation results substantiate the applicability of all the methods to the dc system. Brief comparative analysis with the di/dt method is also presented to highlight the advantages of the proposed methods.

Capacitive discharge based transient analysis with fault detection methodology in DC system

The short circuit fault in the voltage source converter-based dc power system typically generates rapidly rising transient current which may have serious repercussions on dc grid operation and health of the integrated power electronic devices. Thus, the dc grid requires a high speed and robust fault detection for reliable system operation. With this regard, this article proposes a fault detection method based on the S transform (ST) with adaptive adjustment. This improved ST is based on frequency-domain and is able to detect the fault condition within 0.3 ms. It consists of high-frequency detection, which is responsible for fast response due to high time resolution, and low-frequency screening which is used to differentiate faults from other transient conditions. Introducing a correction factor into a Gaussian function when computing ST could extract the high-frequency spectrum, while the low frequency spectrum information is still retained. The proposed method is validated with the multiterminal dc system developed in the OPAL-RT-based real-time simulator. Additionally, its performance is tested with the point-to-point experimental dc test bed. Comparative analysis with other popular frequency-domain fault detection methods, namely, wavelet transform and short-time Fourier transform substantiates the effectiveness of this method.

A modular protection system was developed for DC radial microgrids. The novelty of the realised protection system is based on the combination of different fault detection, characterisation and localisation criteria. The protection system was developed based on real‐time monitoring of voltages and currents at different positions within smart modular switches in the DC system. Intelligent modular DC switches were developed for the setup. On the basis of fault detection criteria, protective measures are initiated and appropriate switching signals are sent to the appropriate switches in order to isolate the faulty part. Different fault locations and types have been tested in the laboratory for a 24 V DC test setup. Hence, the selectivity of the new protection concept has been verified.

This paper focuses on analyzing and comparing the performance of two robust fault detection (FD) criteria for discrete-time linear parameter varying (LPV) systems with bounded uncertainties, namely the state estimation error-based criterion and the classical residual-based criterion. First, a new FD criterion for the detection of multiple multiplicative actuator faults is proposed by testing consistency between the state estimation errors and the healthy state estimation error sets on-line. Then, a guaranteed FD condition is established based on set-separation of healthy and faulty invariant sets of state estimation error. Moreover, the generalized minimum detectable fault (MDF) for multiple actuator faults is defined and computed in order to characterize the performance of the two FD criteria. Finally, a proof is provided to compare the conservatism of the FD criterion using state estimation errors with the classical one based on residuals. At the end of this paper, a numerical example is used to illustrate the effectiveness of the obtained results.

Transient analysis in multi-terminal voltage source converter based direct current system: Median mode decomposition

This study investigates the impact of cognitive service technology on the monitoring and identification capabilities within a direct current (DC) system in a power grid. This paper introduces a novel Pattern Recognition Neural Network (PRNN) by integrating a Pattern Recognition Algorithm (PRA) and Convolutional Neural Networks (CNN) to analyse patterns in the DC system's data. With the increasing complexity and interconnectivity of modern power systems, the need for advanced monitoring and identification solutions becomes crucial. Cognitive service technology, known for its adaptability and learning capabilities, offers a promising avenue for enhancing the performance and reliability of DC systems. The research employs a comprehensive approach, incorporating data analysis, modelling, and simulation to assess the effectiveness of cognitive service technology in monitoring and identifying critical parameters within the DC system. The study aims to contribute valuable insights into the application of cognitive services for improving the overall efficiency and resilience of power grids operating on direct current, thereby fostering advancements in smart grid technologies

The increasing adoption of DC microgrids, driven by the integration of renewable energy sources and the need for efficient power systems, necessitates advanced fault detection mechanisms. Traditional fault detection methods, such as Fourier Transform (FT) and Discrete Fourier Transform (DFT), are limited by their assumptions of signal stationarity and inadequate time-localization capabilities, particularly in detecting high-resistance faults. This research paper investigates the integration of Long Short-Term Memory (LSTM) networks with the Hilbert-Huang Transform (HHT) model to address these limitations. The proposed LSTM-HHT approach leverages LSTM’s ability to capture long-term dependencies and time-series patterns, along with HHT’s proficiency in analysing non-linear and non-stationary signals. The integrated model is implemented and tested using MATLAB Simulink to evaluate its performance in practical DC microgrid scenarios. Results demonstrate that the LSTM-HHT approach significantly enhances fault detection accuracy and reliability, particularly for high-resistance faults that are challenging to identify with traditional methods. The empirical validation in simulated environments highlights the model’s effectiveness in accurately detecting and localizing faults, thereby improving the stability and safety of DC microgrids. This research contributes to the development of more resilient and intelligent fault detection systems, supporting the broader adoption of DC microgrids and the transition to sustainable energy systems. The findings underscore the potential of combining advanced signal processing techniques with machine learning to overcome the inherent limitations of conventional fault detection methods, paving the way for further innovations in microgrid management.

In order to reduce the false alarm rate and false alarm rate of grounding fault detection in substation DC system and improve the efficiency of fault detection, a method of grounding fault detection in substation DC system based on particle filter is proposed. The wavelet threshold denoising method is used to remove the noise of the grounding fault signal of the substation DC system. The wavelet packet decomposition method is used to decompose the grounding fault signal of the substation DC system and extract the characteristics of the grounding fault signal of the substation DC system. Based on the particle filter algorithm, the residual smoothing value is used to detect the grounding fault of the DC system in the substation. The experimental results show that the failure detection rate and false alarm rate of the proposed method are low, which has a good fault detection effect.

This paper proposes a cost-effective short circuit fault detection method for a distribution network in the ac grid-connected low-voltage dc (LVDC) microgrid. In this method, a typical dc system is considered where a number of dc/dc, ac/dc power converters and loads are interfaced to the dc link. Further, these loads are dedicated as local loads to the converter based on the locality of the same. The discharge of a filter capacitor (FC) in power converters in the dc system is the foremost indicator of a fault. Such dynamic behavior of FC can be utilized in the identification of a fault. This method is a time-frequency domain-based wavelet transform (WT), which utilize the current dynamics of an FC for quantitative analysis of a fault current. Additionally, a detailed thresholds selection process is also explained. MATLAB/Simulink model is used to carry out simulation studies. The experimental validation of WT based fault detection method is executed on the ac grid-connected LVDC microgrid testbed interfaced to the dSPACE 1104 controller.

Fault detection has drew much attention nowadays, as it can save time and operational maintenance costs, especially in the wind turbine (WT) that is becoming familiar with renewable energy. Machine learning became widespread use in fault detection methods. However, most available machine learning needs more data and much time to train. Therefore, there is a need to detect faults using a few data during the training process. This paper aims to apply Automated Machine Learning (AutoML) method for fault detection in WT systems. The fault detection in the WT system focuses on the internal stator fault in the generator as it is the main part of the WT. The AutoML model was developed using a neural network (NN) algorithm in python based on the Auto-Keras model. The model was developed using four inputs, i.e. stator and rotor currents in the d-q axis (Iqs, Ids, Iqr and Idr ) while the outputs are impedance values, i.e. stator resistance, Rs , and stator inductance, Ls . The WT system used in this research is the doubly-fed induction generator (DFIG) in MATLAB/Simulink. In the Auto-Keras model, the impedance values (Rs and L s) indicated the condition of the DFIG, either normal or fault conditions. Two fault types were applied to the WT system, i.e. inter-turn short circuit and open circuit fault. The Auto-Keras model was trained and tested with the various values of data. The accuracy and the root means square error (RMSE) value of the model were calculated. The result shows that the accuracy is high as it is more than 93% in most conditions, and the RMSE value is low, close to the zero value. Applying the AutoML method in fault detection of the WT system shows its capability to identify faults accurately.

For multiterminal or meshed Voltage Source Converter (VSC) High-voltage Direct Current (HVDC) systems, high speed protection against DC faults is essential, as power electronic components cannot withstand the rapidly increasing fault currents which would otherwise result. Recently proposed DC fault detection methods were developed based on time domain simulations in EMT-type software, which requires considerable modeling and computational efforts and results in methods specifically designed for the HVDC grid under study. To simplify the initial design of DC fault detection methods, this paper proposes general guidelines based on fundamental theory and offers a reduced modeling approach. Furthermore, the impact of non-ideal measurements is investigated and a method to choose the filters that optimally discriminate these fault signals from noise, is proposed. The approach was evaluated in a case study on fault detection in a realistically dimensioned HVDC grid. The paper shows that the initial design of fast fault detection methods can be based on the relatively simple proposed guidelines and reduced models. The paper furthermore shows that a sufficiently high sampling frequency and a filter matched to the fault signal enable fault detection within hundreds of microseconds and discrimination of DC faults from transients not related to DC faults.

The rapid and effective identification of DC faults is one of the key technologies for the development of multi-terminal DC (MTDC) system based on voltage source converter (VSC). The DC system fault current is characterized of high increasing speed and wide influence range. At present, the peak current and the rising speed of the fault current can be limited by current-limiting circuits, which are serially connected at both ends of the DC line. In this paper, the DC fault characteristics of a three-terminal DC system with DC line current-limiting reactors are analysed. On this basis, a fast identification method for DC faults in MTDC system according to the voltage across the line current-limiting reactor is proposed. The proposed scheme can quickly identify the fault location, the fault type and the fault pole. Finally, the effectiveness of the proposed fault detection scheme is verified by simulations. The simulation results show that the proposed method is still effective when the fault resistance and fault distance change, and is able to realize the rapid and accurate detection of DC faults.

Fault detection and classification are crucial procedures for electric power distribution systems because they can minimize the occurrence of faults. The methods for fault detection and classification have become more problematic because of the significant expansion of distributed energy resources in distribution systems and the change in their currents due to the action of short-circuiting. In this context, to fill this gap, this study presents a robust methodology for short-circuit fault detection and classification with the insertion of distributed generation units. The proposal methodology progresses in two stages: in the former stage, the detection is based on the continuous analysis of three-phase currents, whose characteristics are extracted through maximal overlap discrete wavelet transform. In the latter stage, the classification is based on three fuzzy inference systems to identify the phases with disturbance. The short-circuit type is identified by counting the shorted phases. The algorithm for short-circuit fault detection and classification is developed in MATLAB programming environment. The methodology is implemented in a modified IEEE 34-bus test system and modeled in ATPDraw with three scenarios with and without distributed generation units and considering the following parameters: fault type (single-phase, two-phase, and three-phase), angle of incidence, fault resistance (high impedance fault and low impedance fault), fault location bus, and distributed generation units (synchronous generators and photovoltaic panels). The accuracy is greater than 94.9% for the detection and classification of short-circuit faults for more than 20,000 simulated cases.