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Cable incipient fault (CIF) is a potential fault which may recur over time and evolve into permanent fault eventually. The identification of CIF can help to reduce the likelihood of a permanent fault and enhance power supply reliability. This paper fully considers the randomness and uncertainty of CIF waveforms, and proposes a CIF identification method using power disturbance waveform feature learning. Firstly, the shallow features are extracted to characterize the transient components of different disturbance current waveforms, by conducting a stationary wavelet transform. Then, a dropout deep belief network (DDBN) is constructed using the extracted shallow features by pre-training and fine-tuning. Finally, the well- constructed DDBN model is used to identify CIF from other similar disturbance events. The performance of the proposed method is verified by simulation data and experimental data, for different disturbance events, such as sub-cycle cable incipient fault and multi-cycle cable incipient fault, as well as other over-current disturbance events such as capacitor switching and inrush current. Both the accuracy and generalization ability are higher than other methods. The proposed method provides new insights on possible applications on monitoring of cable status and timely warning of cable faults.

Abstract As the grid codes of wind turbines note, doubly fed induction generator (DFIG)‐based wind energy conversion system (WECS) should maintain uninterrupted operation during certain voltage faults. To meet the codes, a novel configuration of DFIG‐based WECS is proposed in which a superconducting magnetic energy storage (SMES) and a series transformer are incorporated. The control strategies of the WECS are redesigned to smooth the output power fluctuations and ride through different levels of voltage faults. The grid‐side converter (GSC) is controlled to smooth power fluctuations caused by random wind speeds under normal conditions. The GSC and protection circuit are controlled to switch into the series compensation mode to fully compensate the stator voltage during voltage swells and slight voltage sags. While under severe voltage sags, the protection circuit operates in the current limiting mode, which significantly limits the stator and rotor fault overcurrent, improves the stator voltage and supresses fluctuations of the electromagnetic torque. The different faults are applied, the simulation results demonstrate that the presented scheme can effectively smooth output power fluctuations and strengthen the voltage fault ride‐through capability (FRT) of WECS.

Scarce fault ride through capacity and unsteady output power are two vital problems in various in-grid doubly fed induction generators (DFIGs). This paper investigates an integrated DFIG protection scheme with a modified superconducting magnetic energy storage-fault current limiter (SMES-FCL). Based on proper parameter estimation of superconducting coil (SC) inside the SMES-FCL, modified controls of the SC and rotor side converter (RSC) are presented. Simulation results obtained from a 1.5 MW DFIG-based wind turbine case show that the proposed scheme is capable of limiting the peak values of fault rotor current, DC-link voltage and electromagnetic (EM) torque under symmetrical and asymmetrical faults, and thus to protect the RSC, gearbox and mechanical parts of the whole DFIG. With an additional positive-sequence d-q current modification control, this scheme is also discussed to lower the SC current capacity, to shorten the recovery time after grid faults, and to smooth the active power of the DFIG during normal operations. [ABSTRACT FROM AUTHOR]

Catastrophic accidents in power system are characterized by complex and significant uncertainties. The existing risk assessment methods based on probability theory require adequate historical statistic data to ensure the precision of probability model and parameter identification regardless of the rareness of catastrophic accidents. Alternatively, the catastrophic accident can be treated as a fuzzy event, and then a new uncertain risk assessment model to identify the most possible catastrophic accident sequences is presented in this paper. According to physical propagation process and mathematical modeling principle of the catastrophic accident, a risk evaluation system consisting of the credibility measure, global fuzzy severity measure and risk measure is provided based on the credibility theory. The risk assessment model starts from N-1 contingencies, and the most possible catastrophic accident sequences are identified by the risk ranking of all N-k contingencies in each stage. Some numerical cases of the WSCC 9-bus and practical regional power system are simulated to demonstrate the applicability, validity and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Voltage sag caused by faults on an electric power transmission line is one of the most intractable power quality issues for both utility companies and customers. The fault in power system randomly exists along transmission lines due to the combination of many uncertain factors. To predict and assess the annual expected sag frequency (ESF) deriving from the faults along lines, a stochastic-based method that employs maximum entropy principle, namely the maximum entropy probability method (MEPM), has been introduced in this paper. Moreover, various types of faults have been considered systematically. With the fault line intervals and the sample moments taken into account, the discrete values of distribution probability of fault locations along the transmission lines have been estimated by means of the MEPM. For a given voltage sag magnitude corresponding to the voltage tolerant level of sensitive equipment at the tested bus, the ESF has been calculated in view of the statistical fault rates of interrelated transmission lines. The implementation and application to a classical five-bus system and IEEE RTS-30 test system have been presented consequently. The simulations have revealed that compared with the four existing methods, the MEPM is accurate, flexible, and immune of round-off errors, and therefore it can be widely applied to the various aspects of power systems. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]