This work proposes a signal-based fault location method combining both time and frequency-domain features. The method is based on the Traveling Wave (TW) detection and analysis, and only employs 100 microseconds of data around the TW time-of-arrival to the measuring location. Time-domain features are extracted using Mathematical Morphology, while frequency-domain features are obtained using the Stationary Wavelet Transform. The IEEE 34 nodes system is selected for demonstration. TW detection is performed using a method based on Dynamic Mode Decomposition. This work exhaustively analyzes the performance of the proposed method in both fault location classification and regression tasks. Combining both time and frequency-domain features is proven to be more effective than using each type of feature independently. In addition, the sensitivity to noisy measurements and lesser amount of training data are analyzed as well. The performance of this method is compared to other works in the same test system, showing better accuracy than other signal-based methods. Furthermore, the proposed method has a low fault location estimation error rate across the entire length of the feeder and shows a similar performance to slower phasor-based fault location methods.

This paper explores the most important factors that define the Traveling Wave (TW) propagation on distribution systems. The factors considered in this work are: the distance to the fault location, the fault type, and the crossing of system elements (such as regulators, capacitor banks, laterals, and extra loads within the protection zones). This work uses a realistic, yet simplified, distribution system composed of two protection zones, in which, several combinations of the previously mentioned factors are considered. The simulated fault measurements undergo a signal processing stage in which, first, they are decomposed into independent modes using the Karrenbauer transform. Second, a time–frequency representation is obtained using the Stationary Wavelet Transform (SWT), dividing the signal into several frequency bands. Finally, the Parseval’s Energy (PE) theorem is applied to calculate the signal energy in each frequency band. A qualitative analysis is performed based on the previously calculated energies to outline which are the factors that most affect the TW energy during propagation. The results show that distance, the presence of regulators, either in the propagation path or upstream, and the type of fault are the main factors that affect TW propagation across the system, and therefore they should be considered for TW-based protection schemes for distribution systems.

This work provides a detailed protection analysis of a fast, Traveling-Wave (TW), Machine-Learning (ML), local, non-directional, economic, and setting-less protection scheme. The goal is to provide an objective evaluation of how a data-driven TW protection scheme would perform when deployed on a power distribution system as a faster alternative to over-current (OC) protection. Fault simulations consider scenarios from none to high-penetration of renewable energy resources to show its suitability for future applications on Distributed Energy Resources (DER)-dominated distribution systems. A modified IEEE 34 node system with solar Photovoltaic (PV) in multiple locations is modeled in PSCAD/EMTDC. The TW, ML method’s protection scheme is built upon an efficient signal-processing stage, using the Discrete Wavelet Transform, and scaled-down Random Forest models that classify the fault location into several protection zones. The analysis is focused on the quantification of the sensitivity and selectivity of the proposed protection scheme. Furthermore, estimations of the false trip probability and average unnecessary load loss are included, as these events may occur due to the misoperation and miscoordination of the proposed data-driven, signal-based relays. Results show high TW detection rates and fault location accuracies, which cause a small and bounded percentage of imprecise fault locations and unnecessary load loss. Similarly, the same IEEE 34 node system is modeled in OpenDSS and a custom OC protection scheme is designed. The comparison between both methods leads to the conclusion that TW, ML methods can be a significantly faster aid to traditional protection.

Microgrids (MGs) are driving us toward more resilient power grids. They can operate independently from the upstream power grids and provide a reliable source of power to their customers. Conventionally, ac MGs have been deployed to increase the reliability and resilience of power grids or provide power to remote areas where connection to an electric power grid is not possible. However, more recently, dc MGs have gained much attention due to a number of advantages. These advantages include a higher efficiency and power transfer ratio compared to their ac counterparts as well as simpler control and operation since, in dc systems, only voltage and power are the control objectives; the reactive power and frequency of ac systems are not of concern anymore. However, the dc MG still faces some control challenges, especially from the perspective of voltage regulation and power sharing. Moreover, the protection of dc MGs is more challenging than that of ac ones.

This work proposes and develops an implementation of a fault location method to provide a fast and resilient protection scheme for power distribution systems. The method analyzes the transient dynamics of traveling waves (TWs) to generate features using the discrete wavelet transform (DWT), which are then used to train several graph convolutional network (GCN) models. Faults are simulated in the IEEE 34-node system, which is divided into three protection zones (PZs). The goal is to identify the PZ in which the fault occurs. The GCN models create a distributed protection scheme, as all nodes are able to retrieve a prediction. Given that message-passing between nodes occurs both during training and in the execution of the model, the resiliency of such schemes to communication losses was analyzed and demonstrated. One of the models, which only uses voltage measurements, was implemented on a Texas Instruments F28379D development board. The execution times were monitored to assess the speed of the protection scheme. It is shown that the proposed method can be executed in approximately a millisecond, which is comparable to existing TW protection in the transmission system. For experimental purposes, a DWT-based detection method is employed. A design of a setup to playback TWs using two development boards is also addressed.

Pumped Storage Hydropower (PSH) is one of the most popular energy storage technologies in the world. It uses an upper reservoir to store water which can be later used during high-demand. In the United States, most of the energy storage capability actually corresponds to PSH. Moreover, PSH also brings multiple benefits to grid operation. This report presents the Simulink models of three common PSH technologies: Fixed-Speed (FS), Variable-Speed (VS), and Ternary (T)-PSH. These models are available to the general public on this GitHub repository 1 , which contains the MATLAB model initialization files, the Simulink model files, and supplementary MATLAB code used to obtain the figures in this work. For each PSH model, an introductory description of the model components and other relevant functionalities are provided. For further information regarding the models and the initialization parameters, the reader is referred to the shared files in the repository. This report also presents the dynamic behavior of each model. The response of such models to a load event is analyzed and matched with each model?s features. A custom IEEE 39 bus case is employed for the FS and T-PSH simulations, while the VS-PSH is simulated on a simplified three-bus test system due to the computational complexity of the model. For the T-PSH, the steady-state and the switching between several operating modes are also studied in this work. * Send correspondence to mjimene@sandia.gov . 1 See: https://github.com/sandialabs/Simulink_PumpedStorageHydropower .