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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 721 records · Page 40
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DOE OSTI2018

Improving the Convergence Rate of Parareal-in-time Power System Simulation using the Krylov Subspace

The performance of parareal-in-time algorithms is determined on the number of sequential, coarse step iterations. A common tradeoff in designing an efficient parareal-in-time algorithm is between accuracy of the coarse solver and the number of iterations. Traditional parareal implementation for the power system simulation can also have difficulties handling complex power systems. In this paper, we propose a Krylov subspace enhanced parareal algorithm to reduce the number of coarse iterations. The proposed approach is demonstrated on a single-machine-infinite-bus system and the IEEE 10-machine 39-bus system. Noticeable decrease of number of iterations is observed in both cases.

Duan, Nan↗
DOE OSTI2018

A Novel Approach for Voltage Control in Electrical Power Distribution Systems

This paper proposes a novel approach for voltage control in 3-phase unbalanced distribution systems. The approach is based on the change of the magnetizing reactances of the voltage regulators in the system. We used an augmented Lagrangian-based optimization model to determine the optimal settings on a modified IEEE 123 Bus Test System with large PV penetration. Simulations were performed on a minute-based resolution either by controlling regulators or by controlling the magnetizing reactances of the regulators only. The simulation results presented and their comparison show that the control of the magnetizing reactances of the regulators may improve the voltage profile, and may be an addition or alternative to both traditional voltage control devices, such as capacitors, tap changers, and newer devices such as smart inverters.

Ceylan, Oguzhan↗
DOE OSTI2018

Hidden Moving Target Defense against False Data Injection in Distribution Network Reconfiguration

This paper introduces Moving Target Defense (MTD) in distribution system against False Data Injection (FDI) attacks on the supervisory control and data acquisition (SCADA) system. Based on the AC power flow model, a hidden MTD (HMTD) strategy is constructed in combination with network reconfiguration by minimizing the system loss and line power flow differences before and after the HMTD. The proposed HMTD-based network reconfiguration is formulated as a mixed-integer nonlinear programming (MINLP) problem. A refined Genetic Algorithm (GA) is proposed to solve it. Numerical test is conducted in a modified IEEE-66 bus system. The simulation results show that the proposed model reduces the power loss introduced by the HMTD as well as yields a stealthy MTD to the attackers. The impact of HMTD on the system performance is also compared with that of the existing MTD strategies.

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DOE OSTI2018

Impact of Building-Level Motor Protection on Power System Transient Behaviors

Transmission and distribution system level protection strategies have been extensively investigated to advise better coordination of physical protection devices. A diversity of functional motors with dedicated protection schemes are popularized in increasing number of commercial, residential and industrial buildings. This paper focuses on implementing several most popular protection schemes on Eletro-Magnetic Transient Program (EMTP) simulation for three-phase and single-phase induction motors that exist in commercial buildings connected to typical distribution feeders. Voltage depressions are played in at the head of the feeder fully loaded with functional induction motors to investigate the behaviors of single-phase motor stalling, the action of motor protection and reconnection, and the impacts of device-level protection on system-level dynamics. Several distribution feeders are represented in standard IEEE 39-bus transmission system to simulate fault-induced delayed voltage recovery (FIDVR) and explore mitigation strategies by optimally configuring the building-level motor protection settings.

Liu, Yuan↗
DOE OSTI2018

Deep Convolutional Neural Networks for Distribution System Fault Classification

Faults happen very frequently in distribution systems. Identifying fault types and phases are of critical importance for outage management, fault location, and service restoration. However, this task becomes very challenging due to measurement scarcity in distribution systems. This paper is among the first few that applies deep learning techniques in distribution system fault classification. Specifically, a sequential Convolutional Neural Network(CNN)-based classifier is developed to identify fault buses and phases. The input to the CNN is the steady-state voltage and current data measured at substations. The fault identification is modeled as a multi-label classification problem. Training data under various fault scenarios are obtained in OpenDSS and Gaussian noises are added to mimic measurement errors. A case study in IEEE 13-feeder test system is conducted with single and multiple bus faults scenarios. Numerical results demonstrate the high accuracy and fast computation of the proposed deep CNN-based fault classification.

Tian, Guanyu↗
DOE OSTI2018

Restoration using Distributed Energy Resources for Resilient Power Distribution Networks

In this paper, we propose a restorative method to minimize the unserved demand in a power distribution network where some islands are formed due to multiple line outages. The restoration is achieved by tie-line switching and by solving a mixed-integer non-linear problem. Due to combinatorial issues of the optimization problem, a method is proposed which restores the system by setting up microgrids boundaries within the islands and connecting them through tie-lines for exchange of active power in between them. The optimal tie-line to interconnect the microgrids is decided through information exchange between the microgrid operators. Case studies on IEEE 33-bus distribution network is presented to illustrate the results and validate the performance of the proposed method.

Sarkar, Ranadhir↗
DOE OSTI2019

Enhancing Distribution System Resiliency through a Novel Transactive Energy Systems Framework

Restoration of end-use loads during bulk transmission system outages is a key challenge for electric utilities. During those outages, utilities often do not have enough resources to fully restore the end-use loads. Additionally, not all available switching options are feasible due to voltage and load limits. Incentivizing non-utility flexible assets to provide their flexibility during resiliency events is a potential solution to improve resiliency. This paper presents a new transactive energy system framework (TESF) to incentivize distributed energy resources (DERs) to provide reactive power for supporting the self-healing functions of distribution management system (DMS). TESF engages non-utility DERs during resiliency events by incentivizing them through transactive signals computed using marginal cost of producing reactive power and marginal benefit of additional switching operations. TESF calculates additional switching options, which DMS then leverages to identify new optimal switching plans to restore additional loads. Test system consisting of multiple IEEE-13 node distribution feeders is used to demonstrate the performance of the proposed method. For the given system configuration and use-case, TESF results in 24.82 % additional load restoration and one additional switching operation.

Distribution system, reconfiguration, resiliency, ↗
DOE OSTI2019

Evaluation of Anomaly Detection for Wide-Area Protection Using Cyber Federation Testbed

Cyber physical security research for smart grid is currently one of the nation's top R&D priorities. The existing vulnerabilities in the legacy grid infrastructure make it particularly susceptible to countless cyber-attacks. There is a growing emphasis towards building interconnected, sophisticated federated testbeds to perform realistic experiments by allowing the integration of geographically-dispersed resources in the dynamic cyber-physical environment. In this paper, we present a cyber (network) based federation testbed to validate the performance of an anomaly detector in context of a Wide Area Protection (WAP) security. Specifically, we have utilized the resources available at the Iowa State University Power Cyber (ISU PCL) Laboratory to emulate the substation and local center networks; and the US Army Research Laboratory (ARL); to emulate the regional control center network. Initially, we describe a hardware-in-the loop based experimental setup for implementing data integrity attacks on an IEEE 39 bus system. We then perform network packet analysis focusing on latency and bandwidth as well as evaluate the performance of a decision tree based anomaly detector in measuring its ability to identify different attacks. Our experimental results reveal the computed wide area network latency; bandwidth requirement for minimum packet loss; and successful performance of the anomaly detector. Our studies also highlight the conceptual architecture necessary for developing the federated testbed, inspired by the NASPI network.

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DOE OSTI2020

Impedance-Based Prediction of SSR-Generated Harmonics in Doubly-Fed Induction Generators

Doubly-fed induction generators (DFIGs) are prone to subsynchronous resonance (SSR) with series-compensated transmission lines and experiencing SSR-generated harmonics of wide-ranging magnitudes. This paper presents a large-signal impedance-based approach for the prediction of the magnitude of SSR-generated harmonics in DFIGs. Note that the large-signal impedance of a network shows variation in its impedance response with the magnitude of perturbation at its terminals. It is discovered in this paper that the impedance of a series-compen- sated transmission line starts changing with the magnitude of SSR because of the metal-oxide varistor-based protection of series capacitors in the line. The changing line impedance halts the growth of the SSR beyond a certain point, where the DFIG forms a limit-cycle mode and emits harmonics at the SSR frequency. The discovered role of the protection of series capacitors in SSR not only enables the prediction of SSR-generated harmonics but also can be used for the timely detection of an SSR event to avoid system disruption and sudden loss of bulk generation. The large- signal impedance-based prediction of SSR-generated harmonics in DFIG-based wind and hydro generators is demonstrated using numerical simulations of a 2.5-MVA, 690-V DFIG supplying to a series-compensated transmission line.

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DOE OSTI2019

Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators

In this paper, we propose linear operator theoretic framework involving Koopman operator for the data-driven identification of power system dynamics. We explicitly account for noise in the time series measurement data and propose robust approach for data-driven approximation of Koopman operator for the identification of nonlinear power system dynamics. The identified model is used for the prediction of state trajectories in the power system. The application of the framework is illustrated using an IEEE nine bus test system.

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DOE OSTI2020

A Hierarchical Volt-var Optimization with Discrete Variables in Unbalanced Distribution Systems

This paper proposes a framework to determine the optimal active and reactive power dispatch of distributed photovoltaic (PV) generation, switched capacitors, and voltage regulators in multi-phase unbalanced distribution systems. The objectives of the optimal dispatch are minimization of the energy loss, PV real power curtailment, and switching operations of capacitors and voltage regulators, in addition to elimination of voltage violation and reverse power flow. The optimization problem is formulated in rectangular coordinates as a nonlinear, nonconvex problem with discrete variables. A hierarchical twostage framework is proposed to effectively handle those discrete variables and reduce the computational time compared to the unified approach in which all variables are solved simultaneously. The efficacy of the proposed approach and the accuracy of the obtained numerical solution is validated using the unbalanced multi-phase IEEE 34-bus with 15-minute load and PV data.

Nguyen, Quan H.↗
DOE OSTI2019

Synchrophasor Calculation Algorithm Based on Robust State Estimation

This paper proposes a novel robust state estimation based method for synchrophasor calculation. The proposed method upgrades traditional weighted least square based state estimation to a robust state estimation that uses iteratively re-weighted least square. As a result, it can better filter out measurement noise and reject bad data from synchrophasors. Numerical experiments are tested to compare the proposed method with existing IEEE C37.118 standard. Extensive results have demonstrated the great accuracy and dependability of proposed method when processing synchrophasor with measurement noises or bad data.

Li, Yunting↗
DOE OSTI2019

A New Multi-Scale State Estimation Framework for the Next Generation of Power Grid EMS

Accurate system state information under various operation conditions is a prerequisite for power grid monitoring and efficient control. To achieve that goal, a new multi-scale state estimation framework is proposed, paving the way for the development of next generation of energy management system (EMS). The developed framework consists of three key components, namely the static state estimation (SSE) module, the dynamic state estimation (DSE) module, the interfaces and switching logics between the two modules. Specifically, the singular spectrum analysis (SSA)-based change point detection approach is developed to monitor the system continuously. If no event is detected by the SSA, the robust SSE using both SCADA and PMU measurements is executed. Otherwise, the event is declared and the results from SSE are used to derive the initial condition for DSE. During the transient process, only PMU-based DSE is executed for system monitoring and it will be terminated when SSA does not detect any change point of the system. After that, the DSE results are forwarded for SSE initialization and bus voltage magnitude and angle estimations. Simulation results carried out on the IEEE 39-bus system demonstrate the effectiveness and benefits of the proposed framework.

Power system state estimation, energy management s↗
DOE OSTI2020

Robust Matrix Completion State Estimation in Distribution Systems

Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.

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DOE OSTI2020

Data-Driven Load Diversity and Variability Modeling for Quasi-Static Time-Series Simulation on Distribution Feeders

This paper presents a data-driven load modeling methodology for distribution system quasi-static time-series (QSTS) simulation considering both diversity and variability characteristics of distribution loads. Based on our previous work in [1]-[2], a variability library and diversity library have been established based on the realistic high-resolution data collected from actual utility feeders. Given the load profile for the start-of-circuit load of a feeder, the loads on the feeder nodes can be modeled with both diversity and variability instead of being directly scaled from the substation load profile according to the distribution allocation factors. With diversified load models, the load-induced impact on the feeder operation characteristics, such as voltage ramp and regulator operations, can be better considered in QSTS simulation. The proposed modeling methodology has been tested on both the IEEE 123-bus feeder and an actual utility feeder model, and the simulation results have demonstrated the merits of deploying the proposed load modeling methodology.

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DOE OSTI2020

Data-Driven Preemptive Voltage Monitoring and Control Using Probabilistic Voltage Sensitivities

Increased penetration levels of distributed variable renewable generation can cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But because of the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network are needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69-node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time-series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.

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DOE OSTI2019

Impedance-Based Characterization of Power System Frequency Response: Preprint

This paper presents an impedance-based noninvasive approach for the characterization of power system frequency response in real time in the absence of a transient event. It measures transfer function response from the injected active power to the frequency at the point of interconnection for the estimation of inertia and primary frequency response of the system. The so-called frequency response function also shows the effects of the speed of primary frequency control. The paper also derives the relationship between the frequency response function and the network impedance seen from the point of interconnection. This relationship highlights how impedance captures the frequency response behavior of a network. The proposed methodology is demonstrated on a modified IEEE 9-bus system with 25% penetration of wind and PV generation; a 5-MW battery energy storage system is used for the injection of active power perturbations for the measurement of the frequency response function.

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