Abstract:With the rapid deployment of the advanced metering infrastructure (AMI) and distribution automation (DA), self-healing has become a key factor to enhance the resilience of distribution networks. Following a permanent fault occurrence, the distribution network operator (DNO) implements the self-healing scheme to locate and isolate the fault and to restore power supply to out-of-service portions. As an essential component of self-healing, service restoration has attracted considerable attention. This paper mainly reviews the service restoration approaches of distribution networks, which requires communication systems. The service restoration approaches can be classified as centralized, distributed, and hierarchical approaches according to the communication architecture. In these approaches, different techniques are used to obtain service restoration solutions, including heuristic rules, expert systems, meta-heuristic algorithms, graph theory, mathematical programming, and multi-agent systems. Moreover, future research areas of service restoration for distribution networks are discussed.

Abstract:This paper proposes a simple and fast way to determine the direction of a fault in a multi-terminal high voltage direct current (HVDC) grid by comparing the rate of change of voltage (ROCOV) values at either side of the di/dt limiting inductors at the line terminals. A local measurement based secure and fast protection method is implemented by supervising a basic ROCOV relay with a directional element. This directional information is also used to develop a slower communication based DC line protection scheme for detecting high resistance faults. The proposed protection scheme is applied to a multi-level modular converter based three-terminal HVDC grid and its security and sensitivity are evaluated through electromagnetic transient simulations. A methodology to set the protection thresholds considering the constraints imposed by the breaker technology and communication delays is also presented. With properly designed di/dt limiting inductors, the ability of clearing any DC transmission system fault before fault currents exceeds a given breaker capacity is demonstrated.

Abstract:Traditional protection methods are not suitable for hybrid (cable and overhead) transmission lines in voltage source converter based high-voltage direct current (VSC-HVDC) systems. Accordingly, this paper presents the robust fault detection, classification, and location based on the empirical wavelet transform-Teager energy operator (EWT-TEO) and artificial neural network (ANN) for hybrid transmission lines in VSC-HVDC systems. The operational scheme of the proposed protection method consists of two loops ① an EWT-TEO based feature extraction loop, ② and an ANN-based fault detection, classification, and location loop. Under the proposed protection method, the voltage and current signals are decomposed into several sub-passbands with low and high frequencies using the empirical wavelet transform (EWT) method. The energy content extracted by the EWT is fed into the ANN for fault detection, classification, and location. Various fault cases, including the high-impedance fault (HIF) as well as noises, are performed to train the ANN with two hidden layers. The test system and signal decomposition are conducted by PSCAD/EMTDC and MATLAB, respectively. The performance of the proposed protection method is compared with that of the traditional non-pilot traveling wave (TW) based protection method. The results confirm the high accuracy of the proposed protection method for hybrid transmission lines in VSC-HVDC systems, where a mean percentage error of approximately 0.1% is achieved.

Abstract:In this paper, a fast fault detection scheme for voltage source converter based high-voltage direct current (VSC-HVDC) transmission systems is proposed. Based on Bergeron model equations, the remote terminal voltage of an adopted transmission system is calculated in terms of the local measured current and voltage signals. Subsequently, the computed voltage of the remote terminal is compared with the corresponding actual measured-communicated value. Provided that the considered transmission system is functioning well, the difference between the computed and measured voltages is almost zero. However, a considerable virtual voltage arises for fault conditions. When the voltage difference exceeds a predetermined threshold, a fault condition can be detected. Although a reliable communication link is required, the delay for detecting the fault is not caused by the communication time. For evaluation purpose, a detailed simulation is developed using PSCAD/EMTDC with various fault locations, including the cases near the inside or outside of the protected transmission system. The results corroborate a fast detection scheme depending on a moderate sampling/processing frequency level. A high security level is verified even with the worst external faults, or with the misaligned measured samples at the terminals. This corroborates the suitability of the proposed scheme for protecting multi-terminal HVDC systems.

Abstract:High-impedance faults (HIFs) in distribution networks may result in fires or electric shocks. However, considerable difficulties exist in HIF detection due to low-resolution measurements and the considerably weaker time-frequency characteristics. This paper presents a novel HIF detection method using synchronized current information. The method consists of two stages. In the first stage, joint key characteristics of the system are extracted with the minimal system prior knowledge to identify the global optimal micro-phase measurement unit (μPMU) placement. In the second stage, the HIF is detected through a multivariate Jensen-Shannon divergence similarity measurement using high-resolution time-synchronized data in μPMUs in a high-noise environment. l2,1 principal component analysis (PCA), i.e., PCA based on the l2,1 norm, is applied to an extracted system state and fault features derived from different resolution data in both stages. An economic observability index and HIF criteria are employed to evaluate the performance of placement method and to identify HIFs. Simulation results show that the method can reliably detect HIFs with reasonable detection accuracy in noisy environments.

Abstract:With the integration of distributed generation (DG) into a microgrid, fault detection has become a major task to accomplish. A scheme for microgrid feeder protection based on a newly proposed feature, ΔRP, defined as the ratio of the sum of positive-sequence real power (PSRP) at the two ends of the feeder to the larger of the PSRPs among the two ends, is proposed. If ΔRP is larger than a threshold, an internal fault is detected; otherwise, the fault is external or there is no fault. The proposed scheme is tested in various scenarios, including fault type, fault resistance, fault location, fault inception angle, varying DG penetration levels, and simultaneous, evolving, and composite faults. In addition to this, the proposed scheme offers a robust performance when subjected to noise, synchronization error, changes in sampling frequency, and changes in the topology of a microgrid. The dominancy of the proposed scheme is proven by a comprehensive comparative study with various available recent schemes. Test results on the IEEE 13-bus network indicate the viability of the proposed protection scheme for a microgrid. Finally, the proposed scheme has been validated on a real-time simulator.

Abstract:Modern fault-resilient microgrids (MGs) require the operation of healthy phases during unbalanced short-circuits to improve the system reliability. This study proposes a differential power based selective phase tripping scheme for MGs consisting of synchronous and inverter-interfaced distributed generators (DGs). First, the differential power is computed using the line-end superimposed voltage and current signals. Subsequently, to make the scheme threshold-free, a power coefficient index is derived and used for identifying faulted phases in an MG. The protection scheme is tested on a standard MG operating in either grid-connected or islanding mode, which is simulated using PSCAD/EMTDC. The efficacy of the scheme is also assessed on the OPAL-RT manufactured real-time digital simulation (RTDS) platform. Further, the performance of the proposed protection scheme is compared with a few existing methods. The results show that the selective tripping of faulted phases in MGs can be achieved quickly and securely using the proposed scheme.

Abstract:This paper presents a novel fault detection and identification method for low-voltage direct current (DC) microgrid with meshed configuration. The proposed method is based on graph convolutional network (GCN), which utilizes the explicit spatial information and measurement data of the network topology to identify a fault. It has a more substantial feature extraction ability even in the presence of noise and bad data. The adjacency matrix for GCN is developed by considering the network topology as an inherent graph. The bus voltage and line current samples after faults are regarded as the node attributes. Moreover, the DC microgrid model is developed using PSCAD/EMTDC simulation, and fault simulation is carried out by considering different possible events that include environmental and physical conditions. The performance of the proposed method under different conditions is compared with those of different machine learning techniques such as convolutional neural network (CNN), support vector machine (SVM), and fully connected network (FCN). The results reveal that the proposed method is more effective than others at detecting and classifying faults. This method also possesses better robustness under the presence of noise and bad data.

Abstract:This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine (SVM) for multi-terminal direct current systems based on modular multilevel converter (MMC-MTDC). The scheme overcomes existing detection difficulties in the protection of long transmission lines resulting from high grounding resistance and attenuation, and also avoids the sophisticated process of threshold value selection. The high-frequency components in the measured voltage extracted by a wavelet transform and the amplitude of the zero-mode set of the positive-sequence voltage are the inputs to a trained SVM. The output of the SVM determines the fault type. A model of a four-terminal DC power grid with overhead transmission lines is built in PSCAD/EMTDC. Simulation results of EMTDC confirm that the proposed scheme achieves 100% accuracy in detecting short-circuit faults with high resistance on long transmission lines. The proposed scheme eliminates mal-operation of DC circuit breakers when faced with power order changes or AC-side faults. Its robustness and time delay are also assessed and shown to have no perceptible effect on the speed and accuracy of the detection scheme, thus ensuring its reliability and stability.