The report summarizes the work and accomplishments of DOE SETO funded project 36533 “Adaptive Protection and Control for High Penetration PV and Grid Resilience”. In order to increase the amount of distributed solar power that can be integrated into the distribution system, new methods for optimal adaptive protection, artificial intelligence or machine learning based protection, and time domain traveling wave protection are developed and demonstrated in hardware-in-the-loop and a field demonstration.
This work proposes a Traveling Wave (TW) detection and identification method that addresses the demanding time and functional constraints that TW-based protection schemes for power distribution systems require. The high-frequency components of continuously sampled voltage signals are extracted using the Discrete Wavelet Transform, and the designed indicator is monitored to detect the TW arrival time. The limitations of the method are explored, such as the effective range of detection and the exposure to TWs originating from non-fault events. Simulations are conducted on the IEEE 34 nodes system, which has been adapted to include capacitor banks and small loads connection events, as well as transformer energization and de-energization events. After the TW detection, a Random Forest classifier has been trained to infer whether the TW is due to a fault or another type of transient. About the results, the proposed method is sensitive to near faults, and faults can be successfully distinguished from other events.
This paper introduces a new microprocessor-based system that is capable of detecting faults via the Traveling Wave (TW) generated from a fault event. The fault detection system is comprised of a commercially available Digital Signal Processing (DSP) board capable of accurately sampling signals at high speeds, performing the Discrete Wavelet Transform (DWT) decomposition to extract features from the TW, and a detection algorithm that makes use of the extracted features to determine the occurrence of a fault. Results show that this inexpensive fault detection system's performance is comparable to commercially available TW relays as accurate sampling and fault detection are achieved in a hundred and fifty microseconds. A detailed analysis of the execution times of each part of the process is provided.
This paper elaborates the results of the hardware implementation of a traveling wave (TW) protection device (PD) for DC microgrids. The proposed TWPD is implemented on a commercial digital signal processor (DSP) board. In the developed TWPD, first, the DSP board's Analog to Digital Converter (ADC) is used to sample the input at a 1 MHz sampling rate. The Analog Input card of DSP board measures the pole current at the TWPD location in DC microgrid. Then, a TW detection algorithm is applied on the output of the ADC to detect the fault occurrence instance. Once this instance is detected, multi-resolution analysis (MRA) is performed on a 128-sample data butter that is created around the fault instance. The MRA utilizes discrete wavelet transform (DWT) to extract the high-frequency signatures of measured pole current. To quantity the extracted TW features, the Parseval theorem is used to calculate the Parseval energy of reconstructed wavelet coefficients created by MRA. These Parseval energy values are later used as inputs to a polynomial linear regression tool to estimate the fault location. The performance of the created TWPD is verified using an experimental testbed.
Distribution systems may experience fast voltage swings in the matter of seconds from distributed energy resources, such as Wind Turbines Generators (WTG) and Photovoltaic (PV) inverters, due to their dependency on variable and intermittent wind speed and solar irradiance. This work proposes a WTG reactive power controller for fast voltage regulation. The controller is tested on a simulation model of a real distribution system. Real wind speed, solar irradiation, and load consumption data is used. The controller is based on a Reinforcement Learning Deep Deterministic Policy Gradient (DDPG) model that determines optimum control actions to avoid significant voltage deviations across the system. The controller has access to voltage measurements at all system buses. Results show that the proposed WTG reactive power controller significantly reduces system-wide voltage deviations across a large number of generation scenarios in order to comply with standardized voltage tolerances.
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, 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.
This report is a summary of a 3-year LDRD project that developed novel methods to detect faults in the electric power grid dramatically faster than today’s protection systems. Accurately detecting and quickly removing electrical faults is imperative for power system resilience and national security to minimize impacts to defense critical infrastructure. The new protection schemes will improve grid stability during disturbances and allow additional integration of renewable energy technologies with low inertia and low fault currents. Signal-based fast tripping schemes were developed that use the physics of the grid and do not rely on communication to reduce cyber risks for safely removing faults.
This paper presents a novel approach for fault location and classification based on combining mathematical morphology (MM) with Random Forests (RF). The MM stage of the method is used to pre-process voltage and current data. Signal vector norms on the output signals of the MM stage are then used as the input features for a RF machine learning classifier and regressor. The data used as input for the proposed approach comprises only a window of 50 µs before and after the fault is detected. The proposed method is tested with noisy data from a small simulated system. These results show 100% accuracy for the classification task and prediction errors with an average of ~13 m in the fault location task.
This paper uses co-located wind and photovoltaic generation, along with battery energy storage, as a single plant and introduces a method to provide a flexible synthetic inertia (SI) response based on plant-wide settings. The proposed controller accounts for variable resources and correctly adjusts device responses when an inverter-based resource (IBR) may become unavailable to provide a consistent plant level SI response. The flexible SI response is shown to adequately replace the lost synchronous inertial response from equivalent conventional generation when IBR penetration is approximately 25% in a small power system. Furthermore, it is shown that a high gain SI response provided by the combined IBR plant can reduce the rate of change of frequency magnitude over 50% from the equivalently rated conventional generation response.
The development of the High-Resolution Wavelet Transform (HRWT) is driven by the need of increasing the high-frequency resolution of widely used discrete Wavelet Transforms (WTs). Based on the Stationary Wavelet Transform (SWT), which is a modification of the Discrete Wavelet Transform (DWT), a novel WT that increases the number of decomposition levels (therefore increasing the previously mentioned frequency resolution) is proposed. In order to show the validity of the HRWT, this paper encompasses a theoretical comparison with other discrete WT methods. First, a summary of the DWT and the SWT, along with a brief explanation of the WT theory, is provided. Then, the concept of the HRWT is presented, followed by a discussion of the adherence of this new method to the WT's common properties. Finally, an example of the application is performed on a transient waveform analysis from a power system fault event, outlining the benefits that can be obtained from its usage compared to the SWT.
Identifying the location of faults in a fast and accurate manner is critical for effective protection and restoration of distribution networks. This paper describes an efficient method for detecting, localizing, and classifying faults using advanced signal processing and machine learning tools. The method uses an Isolation Forest technique to detect the fault. Then Continuous Wavelet Transform (CWT) is used to analyze the traveling waves produced by the faults. The CWT coefficients of the current signals at the time of arrival of the traveling wave present unique characteristics for different fault types and locations. These CWT coefficients are fed into a Convolutional Neural Network (CNN) to train and classify fault events. The results show that for multiple fault scenarios and solar PV conditions, the method is able to determine the fault type and location with high accuracy.
As the legacy distance protection schemes are starting to transition from impedance-based to traveling wave (TW) time-based, it is important to perform diligent simulations prior to commissioning the TW relay. Since Control-Hardware-In-the-Loop (CHIL) simulations have recently become a common practice for power system research, this work aims to illustrate some limitations in the integration of commercially available TW relays in CHIL for transmission-level simulations. The interconnection of Frequency-Dependent (FD) with PI-modeled transmission lines, which is a common practice in CHIL, may lead to sharp reflections that ease the relaying task. However, modeling contiguous lines as FD, or the presence of certain shunt loads, may cover certain TW reflections. As a consequence, the fault location algorithm in the relay may lead to a wrong calculation. In this paper, a qualitative comparison of the performance of commercially available TW relay is carried out to show how the system modeling in CHIL may affect the fault location accuracy.
This paper demonstrates that a faster Automatic Generation Control (AGC) response provided by Inverter-Based Resources (IBRs) can improve a performance-based regulation (PBR) metric. The improvement in performance has a direct effect on operational income. The PBR metric used in this work was obtained from a California ISO (CAISO) example and is fully described herein. A single generator in a modified three area IEEE 39 bus system was replaced with a group of co-located IBRs to present possible responses using different plant controls and variable resource conditions. We show how a group of IBRs that rely on variable resources may negatively affect the described PBR metric of all connected areas if adequate plant control is not employed. However, increasing the dispatch rate of internal plant controls may positively affect the PBR metric of all connected areas despite variable resource conditions.
This paper proposes a framework to explain and quantify how a Traveling Wave (TW)-based fault location classifier, a Random Forest, is affected by different TW propagation factors. The classifier's goal is to determine the faulty Protection Zone. In order to work with a simplified, yet realistic, distribution system, this work considers a use case with different configurations that are obtained by optionally including several common distribution elements such as voltage regulators, capacitor banks, laterals, and extra loads. Simulated faults are decomposed in frequency bands using the Stationary Wavelet Transform, and the classifier is trained with such signals' energy. SHapley Additive exPlanations (SHAP) are used to identify the most important features, and the effect of different fault configurations is quantified using the Jensen-Shannon Divergence. Results show that distance, the presence of voltage regulators and the fault type are the main factors that affect the classifier's behavior.
This paper demonstrates that a faster Automatic Generation Control (AGC) response provided by Inverter-Based Resources (IBRs) can improve a performance-based regulation (PBR) metric. The improvement in performance has a direct effect on operational income. The PBR metric used in this work was obtained from a California ISO (CAISO) example and is fully described herein. A single generator in a modified three area IEEE 39 bus system was replaced with a group of co-located IBRs to present possible responses using different plant controls and variable resource conditions. We show how a group of IBRs that rely on variable resources may negatively affect the described PBR metric of all connected areas if adequate plant control is not employed. However, increasing the dispatch rate of internal plant controls may positively affect the PBR metric of all connected areas despite variable resource conditions.
This paper uses clusters of solar photovoltaic units, wind turbines, and battery energy storage systems as a single controllable plant and proposes a method to enable adaptive plant wide droop control. Each of these clusters is defined as a Flexpower plant. The proposed control is presented with multiple configurations that enable the same overall droop characteristic to the Flexpower plant, but use each of the resource technologies in a different manner. One of the control configurations considers the availability of the resources for wind and solar units, as well as the state of charge of energy storage units, when distributing droop action to each unit that comprise the Flexpower plant. The proposed approaches were tested in a small power system where it is shown that the Flexpower plant can provide frequency regulation to the system in a variety of ways depending on which of the proposed control configurations was selected.
Identifying the location of faults in a fast and accurate manner is critical for effective protection and restoration of distribution networks. This paper describes an efficient method for detecting, localizing, and classifying faults using advanced signal processing and machine learning tools. The method uses an Isolation Forest technique to detect the fault. Then Continuous Wavelet Transform (CWT) is used to analyze the traveling waves produced by the faults. The CWT coefficients of the current signals at the time of arrival of the traveling wave present unique characteristics for different fault types and locations. These CWT coefficients are fed into a Convolutional Neural Network (CNN) to train and classify fault events. The results show that for multiple fault scenarios and solar PV conditions, the method is able to determine the fault type and location with high accuracy.
A novel method for fault classification and location is presented in this paper. This method is divided into an initial signal processing stage that is followed by a machine learning stage. The initial stage analyzes voltages and currents with a window-based approach based on the dynamic mode decomposition (DMD) and then applies signal norms to the resulting DMD data. The outputs for the signal norms are used as features for a random-forests for classifying the type of fault in the system as well as for fault location purposes. The method was tested on a small distribution system where it showed an accuracy of 100% in fault classification and a mean error of ~ 30 m when predicting the fault location.
The paper proposes an implementation of Graph Neural Networks (GNNs) for distribution power system Traveling Wave (TW) - based protection schemes. Simulated faults on the IEEE 34 system are processed by using the Karrenbauer Transform and the Stationary Wavelet Transform (SWT), and the energy of the resulting signals is calculated using the Parseval's Energy Theorem. This data is used to train Graph Convolutional Networks (GCNs) to perform fault zone location. Several levels of measurement noise are considered for comparison. The results show outstanding performance, more than 90% for the most developed models, and outline a fast, reliable, asynchronous and distributed protection scheme for distribution level networks.