Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Smart grid technologies and wide-spread installation of advanced metering infrastructure (AMI) equipment present new opportunities for the use of machine learning algorithms paired with big data to improve distribution system models. Accurate models are critical in the continuing integration of distributed energy resources (DER) into the power grid, however the low-voltage models often contain significant errors. This paper proposes a novel spectral clustering approach for validating and correcting customer electrical phase labels in existing utility models using the voltage timeseries produced by AMI equipment. Spectral clustering is used in conjunction with a sliding window ensemble to improve the accuracy and scalability of the algorithm for large datasets. The proposed algorithm is tested using real data to validate or correct over 99% of customer phase labels within the primary feeder under consideration. This is over a 94% reduction in error given the 9% of customers predicted to have incorrect phase labels.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Quasi-static time-series (QSTS) simulation provides an accurate method to determine the impact that new PV interconnections including control strategies would have on a distribution feeder. However, the QSTS computational time currently makes it impractical for use by the industry. A vector quantization approach [1- 2] leverages similarities in power flow solutions to avoid re-computing identical power flows resulting in significant time reduction. While previous work arbitrarily quantized similar power flow scenarios, this paper proposes a novel circuit-specific quantization algorithm to balance speed and accuracy. This sensitivity-based method effectively quantizes the power flow scenarios prior to running the quantized QSTS simulation. The results show vast computational time reduction while maintaining specified bounds for the error.

Fast deployment of renewable energy resources in distribution networks, especially solar photovoltaic (PV) systems, have motivated the need for inverter-based voltage regulation. Integration studies are often necessary to fully understand the potential impacts of PV inverter settings on the various elements of the distribution system, including voltage regulators and capacitor banks. A year long quasi-static time series (QSTS) at second-level granularity provides a comprehensive assessment of these impacts, however the computational burden associated with running QSTS limits its applicability. This paper proposes a fast QSTS simulation technique capable of modeling the smart inverter dynamic VAR control functionality and accurately estimating the states of controllable elements including voltage regulators and capacitor banks at each time step. Consequently, the complex interactions between various legacy voltage regulation devices is also captured. The efficacy of the proposed algorithm is demonstrated on the IEEE 13-bus test case with a 98% reduction in computation time.

The increasing penetration of inverter-interfaced resources underscores the need of valid and accurate pv-inverter models for short circuit studies and for the design of proper protection schemes. This paper presents comparison and validation of several inverter models' dynamics under fault scenarios to two commercial inverters using a Power Hardware-in-the-Loop (PHIL) testbed. Nowadays, IEEE1574 compliant inverters with anti-islanding will contribute for several cycles (1.1 p.u.) before they disconnect. As the inverter standards move towards low voltage ride-through (LVRT) capabilities to counteract remote faults, the accurate modeling of inverters using this feature becomes extremely important. One of the purposes of this paper is to compare the dynamic behavior of different inverter models with LVRT capabilities against two commercial inverters with the aid of PHIL simulation environments. Comparisons were made under different fault scenarios using the IEEE 13 node feeder as testing grid. The other purpose is to raise awareness amongst inverter manufacturers on providing accurate and comprehensive inverter simulation models that account for the protection engineers necessities.

As PV penetration on the distribution system increases, there is growing concern about how much PV each feeder can handle. A total of 14 medium-voltage distributions feeders from two utilities have been analyzed in detail for their individual PV hosting capacity and the locational PV hosting capacity at all the buses on the feeder. This paper discusses methods for analyzing PV interconnections with advanced simulation methods to study feeder and location-specific impacts of PV to determine the locational PV hosting capacity and optimal siting of PV. Investigating the locational PV hosting capacity expands the conventional analytical methods that study only the worst-case PV scenario. Previous methods are also extended to include single-phase PV systems, especially focusing on long single-phase laterals. Finally, the benefits of smart inverters with volt-var is analyzed to demonstrate the improvements in hosting capacity.

Distribution system analysis with high penetrations of distributed PV require quasi-static time-series (QSTS) analysis to model the variability introduced on the distribution system, but current QSTS algorithms are prohibitively burdensome and computationally intensive. This paper proposes a variable timestep algorithm to calculate the critical time periods when QSTS simulations should be solved at higher or lower time-resolution and to backtrack for any critical periods that were missed. This variable time-step solver is a new method of performing timeseries simulations with high accuracy while performing the simulation more than 50 times faster. The scalability of the algorithm is demonstrated using a real utility distribution system model with thousands of buses.

The proliferation of photovoltaic (PV) distributed energy resources (DER) on distribution systems have caused concerns about electric power system (EPS) protection schemes, protection configurations, and device coordination. With the EPS designed for power to flow in one direction, the high penetration of PV-based DER has created concerns of grid reliability and protection scheme efficacy. The short-circuit current characteristics of the classical synchronous generator has been well characterized for symmetrical or unsymmetrical short circuit faults, but inverter-based DER dynamic models are not as wellknown and are generally specific to a single inverter manufacturer. There is also uncertainty in how advanced inverter controls like volt-var and low-voltage ride-through capabilities can impact the inverter fault currents. This paper performs laboratory tests to quantify the fault currents of single-phase, three-phase, and grid-forming inverters under a range of gridsupport function operating modes. The results characterize the PV DER sub-transient, transient, and steady-state equivalents. It was found that grid-support functions affect the current contribution from PV inverters.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Successful system protection is critical to the feasibility of the DC microgrid system. This work focused on identifying the types of faults, challenges of protection, different fault detection schemes, and devices pertinent to DC microgrid systems. One of the main challenges of DC microgrid protection is the lack of guidelines and standards. The various parameters that improve the design of protection schemes were identified and discussed. Due to the absence of physical inertia, the resistive nature of the line impedance affects fault clearing time and system stability during faults. Therefore, the effectiveness of protection coordination systems with communication were also explored. A detailed literature review was done to identify possible grounding schemes and protection devices needed to ensure seamless power flow of grid-connected DC microgrids. Ultimately, it was identified that more analyses and experimentation are needed to develop optimized fault detection schemes with reduced fault clearing time.

High-resolution, quasi-static time series (QSTS) simulations are essential for modeling modern distribution systems with high-penetration of distributed energy resources (DER) in order to accurately simulate the time-dependent aspects of the system. Presently, QSTS simulations are too computationally intensive for widespread industry adoption. This paper proposes to simulate a portion of the year with QSTS and to use decision tree machine learning methods, random forests and boosting ensembles, to predict the voltage regulator tap changes for the remainder of the year, accurately reproducing the results of the time-consuming, brute-force, yearlong QSTS simulation. This research uses decision tree ensemble machine learning, applied for the first time to QSTS simulations, to produce high-accuracy QSTS results, up to 4x times faster than traditional methods.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.