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.
In this paper, the potential for time series classifiers to identify faults and their location in a DC Microgrid is explored. Two different classification algorithms are considered. First, a minimally random convolutional kernel transformation (MINIROCKET) is applied on the time series fault data. The transformed data is used to train a regularized linear classifier with stochastic gradient descent (SDG). Second, a continuous wavelet transform (CWT) is applied on the fault data and a convolutional neural network (CNN) is trained to learn the characteristic patterns in the CWT coefficients of the transformed data. The data used for training and testing the models are acquired from multiple fault simulations on a 750 VDC Microgrid modeled in PSCAD/EMTDC. The results from both classification algorithms are presented and compared. For an accurate classification of the fault location, the MINIROCKET and SGD Classifier model needed signals/features from several measurement nodes in the system. The CWT and CNN based model accurately identified the fault location with signals from a single measurement node in the system. By performing a self-learning monitoring and decision making analysis, protection relays equipped with time series classification algorithms can quickly detect the location of faults and isolate them to improve the protection operations on DC Microgrids.
Efficient restoration of the electric grid from significant disruptions – both natural and manmade – that lead to the grid entering a failed state is essential to maintaining resilience under a wide range of threats. Restoration follows a set of black start plans, allowing operators to select among these plans to meet the constraints imposed on the system by the disruption. Restoration objectives aim to restore power to a maximum number of customers in the shortest time. Current state-of-the-art for restoration modeling breaks the problem into multiple parts, assuming a known network state and full observability and control by grid operators. These assumptions are not guaranteed under some threats. This paper focuses on a novel integration of modeling and analysis capabilities to aid operators during restoration activities. A power flow-informed restoration framework, comprised of a restoration mixed-integer program informed by power flow models to identify restoration alternatives, interacts with a dynamic representation of the grid through a cognitive model of operator decision-making, to identify and prove an optimal restoration path. Application of this integrated approach is illustrated on exemplar systems. Validation of the restoration is performed for one of these exemplars using commercial solvers, and comparison is made between the steps and time involved in the commercial solver, and that required by the restoration optimization in and of itself, and by the operator model in acting on the restoration optimization output. Publications and proposals developed under this work, along with a path forward for additional expansion of the work, and summary of what was achieved, are also documented.
Power systems are subjected to a wide range of disturbances during daily operations. Severe disturbances, such as a loss of a large generator, a three-phase bolted fault on a generator bus, or a loss of a transmission line, can lead to the loss of synchronism of a generator or group of generators. The ability of a power system to maintain synchronism during the few seconds after being subjected to a severe disturbance is known as transient stability. Most of the modern methods of controlling transient stability involve special protection schemes or remedial action schemes. These special protection schemes sense predetermined system conditions and take corrective actions, such as generator tripping or generation re-dispatch, in real time to maintain transient stability. Another method is the use of a real-time feedback control system to modulate the output of an actuator in response to a signal. This paper provides a fundamental evaluation of the use of feedback control strategies to improve transient stability in a power system. An optimal feedback control strategy that modulates the real power injected and absorbed by distributed energy-storage devices is proposed. Its performance is evaluated on a four-machine power system and on a 34-machine reduced-order model of the Western North American Power System. The result shows that the feedback control strategy can increase the critical fault clearing time by 60%, thereby improving the transient stability of the power system.
Interest in the application of DC Microgrids to distribution systems have been spurred by the continued rise of renewable energy resources and the dependence on DC loads. However, in comparison to AC systems, the lack of natural zero crossing in DC Microgrids makes the interruption of fault currents with fuses and circuit breakers more difficult. DC faults can cause severe damage to voltage-source converters within few milliseconds, hence, the need to quickly detect and isolate the fault. In this paper, the potential for five different Machine Learning (ML) classifiers to identify fault type and fault resistance in a DC Microgrid is explored. The ML algorithms are trained using simulated fault data recorded from a 750 VDC Microgrid modeled in PSCAD/EMTDC. The performance of the trained algorithms are tested using real fault data gathered from an operational DC Microgrid located on the Kirtland Air Force Base. Of the five ML algorithms, three could detect the fault and determine the fault type with at least 99% accuracy, and only one could estimate the fault resistance with at least 99% accuracy. By performing a self-learning monitoring and decision making analysis, protection relays equipped with ML algorithms can quickly detect and isolate faults to improve the protection operations on DC Microgrids.
Growing interest in renewable energy sources has led to an increased installation rate of distributed energy resources (DERs) such as solar photovoltaics (PVs) and wind turbine generators (WTGs). The variable nature of DERs has created several challenges for utilities and system operators related to maintaining voltage and frequency. New grid standards are requiring DERs to provide voltage regulation across distribution networks. Volt-Var Curve (VVC) control is an autonomous grid-support function that provides voltage regulation based on the relationship between voltage and reactive power. This paper evaluates the performance of a WTG operating with VVC control. The evaluation of the model involves a MATLAB/Simulink simulation of a distribution system. For this simulation the model considers three WTGs and a variable load that creates a voltage event.