A fault protection and location method for a dc bus microgrid system is presented in this paper. Unlike traditional ac systems, dc bus systems cannot survive or sustain high-magnitude fault currents. And if a fault causes the dc bus to de-energize completely, it makes locating faults very difficult. The main goal of the proposed scheme is to detect and isolate faults in the dc bus without de-energizing the entire system and identifying the fault location. In order to achieve this, a ring-type bus was used in this paper. The bus was segmented into overlapping nodes and links with circuit breakers (CBs) to isolate the segment in the event of a fault. Backup protection is implemented for circuit breaker failures to improve system reliability. A noniterative fault-location technique using a probe power is also presented in this paper. This probe power can also be used for a pilot test before main CB reclosing to avoid system issues that can be expected when the reclosing fails due to a permanent fault. The proposed algorithm can be implemented and executed by an intelligent electrical device for individual node. The proposed concepts have been verified with computer simulations and hardware experiments.

DC Ring-Bus Microgrid Fault Protection and Identification of Fault Location

This paper proposes a fault detection and classification method for medium voltage DC (MVDC) shipboard power systems (SPSs) by integrating wavelet transform (WT) multiresolution analysis (MRA) technique with artificial neural networks (ANNs). The MVDC system under consideration for future all-electric ships presents a range of new challenges, in particular the fault detection and classification issues addressed in this paper. The WT-MRA and Parseval's theorem are employed in this paper to extract the features of different faults. The energy variation of the fault signals at different resolution levels are chosen as the feature vectors. As a result of analysis and comparisons, the Daubechies 10 (db10) wavelet and scale 9 are the chosen wavelet function and decomposition level. Then, ANN is adopted to automatically classify the fault types according to the extracted features. Different fault types, such as short circuit faults on both dc bus and ac side, as well as ground fault, are analyzed and tested to verify the effectiveness of the proposed method. These faults are simulated in real time with a digital simulator and the data are then initially analyzed with MATLAB. The case study is a notional MVDC SPS model, and promising classification accuracy can be obtained according to simulation results. Finally, the proposed fault detection algorithm is implemented and tested on a real-time platform, which enables it for future practical use.

Fault Detection and Classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks

The integration of distributed generation (DG) units into distribution networks has challenged the operating principles of traditional AC distribution systems, and also motivated the development of emerging DC systems. Of particular concern are the challenges associated with the operation of conventional protection schemes and/or devices. This paper first analyses the fault current characteristics in AC and DC distribution systems; it then presents a comprehensive review of the latest protection methods proposed for distribution systems embedding DGs. In addition, the advantages and disadvantages of each method are outlined and compared. The differences between the protection algorithms employed in/proposed for AC and DC systems are also discussed. Finally, this study identifies the future trends and provides recommendation for researches in the field of protections of DC distribution networks.

Protection of AC and DC distribution systems. Embedding distributed energy resources: a comparative review and analysis

Protection techniques for DC microgrid- A review

A fault protection and location method for ungrounded dc traction power systems is presented in this paper. Many dc traction power systems have an ungrounded power circuit to increase the leakage path resistance. Although ungrounded systems can continue operating with a single ground contact, unlike solid- or low-resistance grounded systems, because of the very low fault current, a second ground fault in another pole will result in a line-to-line fault that could cause significant system damage. However, it is difficult to detect the first ground contact due to the low ground current and even harder to locate because it can be seen by many detectors in the network. The proposed scheme in this paper uses a probe unit to detect and locate the first single ground fault in ungrounded traction power systems. The probe unit applies probe voltage to detect the fault and, once the fault is detected, analyzes the response to dc or swept-frequency ac probe voltage to locate the fault. The proposed concepts have been verified with computer simulations and hardware experiments and demonstrated a successful performance.

Ground Fault Detection and Location for Ungrounded DC Traction Power Systems

A method is proposed for the real-time detection of DC-link short-circuit faults in DC transit systems. The discrete wavelet transform is implemented to detect any surges in the DC third-rail current waveform. In the event of a surge the wavelet transform extracts a feature vector from the current waveform and feeds it to a self-organising neural network. The neural network determines whether the feature vector belongs to a normal or a fault current surge.

Real-time detection using wavelet transform and neural network of short-circuit faults within a train in DC transit systems

For effective protection of DC railway systems, it is important to discriminate the remote short-circuit current from the train starting current. Four methods for the discrimination are assessed. The first two methods apply the traditional heuristics by comparing the magnitude and the rate of change (di/dt) between the two currents. The remaining methods use the Fourier and wavelet transforms. It is shown that the first three methods do not provide consistent discrimination. The remote short-circuit current is determined mainly by the steel rail impedance, which is time varying due to the skin effect. In contrast, impedances of the traction motor and the contact wires, and the change of operating mode during starting govern the train starting current. The wavelet transform identifies these salient features. The remote short-circuit current is simulated using EMTP, and a SIMULINK model is used to obtain the train starting current.

Remote short-circuit current determination in DC railway systems using wavelet transform

Simulation of power electronic circuits remains a problem due to the high level of stiffness brought about by the modelling of switches as biresistors i.e. very low turn-on resistance and very high turn-off resistance. The merits and drawbacks of two modelling methods that address this problem are discussed. A modelling solution for ensuring numerically stable, accurate and fast simulation of power electronic systems is proposed. The solution enables easy connectivity between power electronic elements in the simulation model. It involves the modelling of virtual capacitance at switching nodes to soften voltage discontinuity due to the switch current suddenly going to zero. Undesirable ringing effects that may arise due to the interaction between the virtual capacitance and circuit inductance are eliminated by modelling virtual damping resistors in parallel to inductors that are adjacent to switching elements. A midpoint configuration method is also introduced for modelling shunt capacitors. A DC traction system is simulated using this modelling strategy and the results are included. Simulation results obtained using this modelling strategy are validated by comparison with the established mesh analysis technique of modelling. The simulation performance is also compared with the Power System Blockset commercial software.

Fast, accurate and stable simulation of power electronic systems using virtual resistors and capacitors

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Papers

Real-time detection using wavelet transform and neural network of short-circuit faults within a train in DC transit systems

Remote short-circuit current determination in DC railway systems using wavelet transform

Fast, accurate and stable simulation of power electronic systems using virtual resistors and capacitors