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At least 145 records · Page 8

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DOE OSTI2019

OE0000878-Master thesis Observability Analysis of Power Distribution Systems with Distributed Energy Resources Using Correlational Measurements

In this thesis, an observability analysis framework is proposed for power distribution systems. The framework analyzes the sufficiency of the available measurements for monitoring the system. In the areas of the network where sufficient sensors have been deployed, the complex voltage at the nodes may be estimated. The framework also provides a metric that quantifies the accuracy of these voltage estimates. Due to the cost of sensors and the size of distribution systems, it is frequently the case that the available measurements are insufficient for complete observability of the system. In this thesis, the author proposes the use of Correlational Measurements (CMs) for improving distribution system observability by leveraging correlation between loads and between Distributed Energy Resources (DERs). Load correlation due to class-residential, commercial, industrial, etc.- is captured via correlational load measurements, the first type of CM. Injection correlation of DERs-such as wind turbines located in proximity-is captured via correlational DER measurements, the second type of CM. This thesis presents the CM formulation and derives modified node voltage and branch current based state estimators (NVSE and BCSE) accommodating CMs. Additionally, this thesis contains an on-line parameter estimation scheme for CMs that improves the accuracy of state estimates over time. The observability analysis framework with CMs is demonstrated through simulations on the IEEE 123 Node Distribution Test Feeder. It is shown that the introduction of CMs leads to improved observability of the system with only a 0.7% decrease in state estimation accuracy for NVSE and a 0.5% increase in state estimation accuracy for BCSE.

John, Yohan M↗

DOE OSTI2020

Considerations for AMI-Based Operations for Distribution Feeders

More than $5 billion in investments in advanced metering infrastructure (AMI) technologies, AMI deployments, as pervasive secondary network voltage monitoring systems, provide opportunities for utility operations and controls. This paper focuses on the considerations for AMI-based tools and techniques as the industry moves toward operationalizing such large data sets. Phase identification is a first such tool. Numerous distribution network analysis, monitoring, and control applications - including volt/volt-ampere reactive control, state estimation, and distribution automation - require accurate phase connectivity information in the system models. The phase connectivity database maintained by utilities is inaccurate because of a significant amount of missing data, restoration activities, and network reconfiguration. Existing phase identification techniques that estimate phase connectivity work well in distribution feeders that have low or no photovoltaic (PV) generation; however, they fail to identify the phases accurately when considerable PV generation is present. This work addresses the phase identification problem in the presence of high PV generation using statistical analysis methods. Further, insights into the AMI data requirements for this application in terms of data window length and resolution are provided using sensitivity analysis performed on an actual distribution feeder model of San Diego Gas & Electric Company. The results of this study show that the phase connectivity, even in the presence of high PV generation, can be accurately identified using statistical analysis of AMI data of 1 day.

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DOE OSTI2019

Considerations for AMI-Based Operations for Distribution Feeders: Preprint

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DOE OSTI2019

Robust Decentralized State Estimation

The health of a distribution grid is maintained by a distribution management system (DMS). Before the rise of distributed energy resource (DER), the DMS primarily managed the unidirectional power flow through the distribution grid from external sources (e.g. power brought from a large generating plant to the distribution grid by a transmission grid). Now, a typical DMS will utilize distributed energy resource manage systems (DERMS) to account for DER such as rooftop solar, community solar, and wind turbines. Each DERMS is responsible for an area of the distribution grid, issuing commands to the associated real-time automation controller (RTAC) to regulate power flow and maintain health of that section of the grid.

42 ENGINEERING↗

DOE OSTI2023

Efficient Network Partitioning: Application for Decentralized State Estimation in Power Distribution Grids

Increase in the proliferation of distributed energy resources require real-time situational awareness for efficient grid operations. State estimation plays an important role for the real-time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hier-archical spectral clustering-based network partitioning algorithm followed by a decentralized compressive sensing (DCS)-based state estimation. The applicability of the proposed network partitioning algorithm is tested on an IEEE 123-bus network, an IEEE 8,500-node system, and a 6,000+ node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum number of edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6,000+ node distribution network after dividing the network into 18 optimal partitions. Simulation results show that the DCS-based state estimation recovers the system states with high accuracy and low complexity.

alternating direction method of multipliers↗

DOE OSTI2018

Assessing Gaussian Assumption of PMU Measurement Error Using Field Data

Gaussian PMU measurement noise has been assumed for many power system applications, such as state estimation, oscillatory modes monitoring, voltage stability analysis, to cite a few. This letter proposes a simple yet effective approach to validate this assumption by using the stability property of a probability distribution and the concept of redundant measurement. Extensive results using field PMU data from WECC system reveal that the Gaussian assumption is questionable.

Wang, Shaobu (ORCID:0000000236073573)↗

DOE OSTI2010

Using State Estimation Residuals to Detect Abnormal SCADA Data

Detection of manipulated supervisory control and data acquisition (SCADA) data is critically important for the safe and secure operation of modern power systems. In this paper, a methodology of detecting manipulated SCADA data based on state estimation residuals is presented. A framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection process. The BACON algorithm is applied to detect outliers in the state estimation residuals. The IEEE 118-bus system is used as a test case to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.

SCADA, BACON algorithm, state estimation, outlier ↗

DOE OSTI2020

Net Load Redistribution Attacks on Nodal Voltage Magnitude Estimation in AC Distribution Networks

A high penetration level of smart devices and communication networks increases the threat of cyber-attacks in the distribution system. In this paper, we model a hidden, coordinated, net load redistribution attack (NLRA) in an AC distribution system. Based on local information of an attack region, the attacker’s goal is to create violations in nodal voltage magnitude estimation. Acting as a system operator equipped with global AC state estimation and bad data detection, we validate the stealthiness of the hidden NLRA in multiple attack cases. Simulation results on a modified PG&E 69-node distribution system show the validity of the proposed NLRA. The influence of NLRA on the distribution system is assessed and the impact of attack regions, attack timing, and system observability is also revealed.

Zhang, Hang↗

DOE OSTI2024

H ∞ Control for Energy Dispatch in Autonomous Nanogrid With Communication Delays

This paper proposes an optimal controller and estimator for energy dispatch to balance the power supply and demand considering communication delays. The proposed algorithm involves modeling an autonomous nanogrid (ANG) consisting of distributed energy resources, energy storage systems, loads, an $H$ ∞ controller with a reference power modulation technique, and a state estimator. The ANG was developed to express the dynamic supply-demand energy balance of a nanogird system. Reference power modulation was designed to generate the desired ESS power based on the imbalanced energy. Random communication delays were modeled using a stochastic variable satisfying the Bernoulli random binary distribution. The optimal $H$ ∞ controller and estimator were developed using a linear matrix inequality approach to exponentially stabilize the closed-loop system. Simulations were performed using real daily demand forecasts obtained from the Korea Meteorological Administration to demonstrate the effectiveness of the proposed real-time optimization algorithm.

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DOE OSTI2011

Designing a Distributed Systems Architecture Testbed for Real-Time Power Grid Systems

Power engineers who are striving to improve real-time attribute of power grid applications are ill equipped with software engineering methods and tools that allow them to rigorously evaluate their designs, taken into account data communication, geographic locations, and high performance computing capacity. This paper presents a technical approach to designing a testbed for embedding real-time monitoring and computation functionalities into the power grid system. The approach focuses on integrating the parallel computational models with the data management infrastructure for near-real time power grid state estimation. We study and summarize various forces and requirements that drive the design decisions in the distributed systems architecture. Given the continental scale of the power grid, it is important for the testbed to be extensible and scalable within a complex topology of physical entities, controlled by an overlaid network of power utilities and regulatory balancing authorities. This paper outlines the technical steps, and software toolkits to develop this testbed.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2018

Synchrophasor Data Correction under GPS Spoofing Attack: A State Estimation-Based Approach

GPS spoofing attack (GSA) has been shown to be one of the most imminent threats to almost all cyber-physical systems incorporated with the civilian GPS signal. Specifically, for our current agenda of the modernization of the power grid, this may greatly jeopardize the benefits provided by the pervasively installed phasor measurement units (PMU). In this paper, we consider the case where synchrophasor data from PMUs are compromised due to the presence of a single GSA, and show that it can be corrected by signal processing techniques. In particular, we introduce a statistical model for synchrophasorbased power system state estimation (SE), and then derive the spoofing-matched algorithms for synchrophasor data correction against GPS spoofing attack. Different testing scenarios in IEEE 14-, 30-, 57-, 118-bus systems are simulated to show the proposed algorithms’ performance on GSA detection and state estimation. Numerical results demonstrate that our proposed algorithms can consistently locate and correct the spoofed synchrophasor data with good accuracy as long as the system observability is satisfied. The accuracy of state estimation is significantly improved compared with the traditional weighted least square method and approaches the performance under the Genie-aided method.

Phasor measurement units, Global Positioning Syste↗

DOE OSTI2023

Optimal Power Flow With State Estimation in the Loop for Distribution Networks

Here in this article, we propose a framework for running optimal control-estimation synthesis in distribution networks. Our approach combines a primal-dual gradient-based optimal power flow solver with a state estimation feedback loop based on a limited set of sensors for system monitoring, instead of assuming exact knowledge of all states. The estimation algorithm reduces uncertainty on unmeasured grid states based on certain online state measurements and noisy "pseudomeasurements." We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least-squares estimator. The numerical results on a 4521-node network demonstrate that this approach can scale to extremely large networks and provide robustness to both large pseudomeasurement variability and inherent sensor measurement noise.

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DOE OSTI2019

Resilient Reinforcement in Secure State Estimation against Sensor Attacks with a priori Information

This paper deals with the state estimation problem in the presence of malicious sensor attacks. Unlike the existing work, in this paper, we address the problem with a priori information of the state to be estimated. Specifically, we address three prior knowledge: the sparsity information, (alpha, n_0)-sparsity information, and side information, and in each scenario, we show that the state can be reconstructed even if more sensors are compromised. This implies that the prior information reinforces the system resilience against sensor attacks. Then, an estimator under sensor attacks considering the information is developed and, under a certain condition, the estimator can be relaxed into a tractable convex optimization problem. Further, we extend this analysis to systems in presence of measurement noises, and it is shown that the prior information reduces the state-estimation error caused by the noise. Here, the numerical simulations in a diffusion process finally illustrate the reinforcement and error-reduction results with the information.

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DOE OSTI2022

Master State Distributed Estimator (masde)

The MSE technology provides a distributed state estimation scheme for power utilities by making it a real time dynamic system, versus the static snapshot method used today. This will allow utilities to verify power grid readings and identify false data on the communication network. The prevailing method of estimation takes all the data from the system into a single load flow equation at the utilities command center. This becomes a large algorithm that is very time consuming to solve, providing the engineers only snap shots of the system. The MFD distributes an algorithm to devises that are already installed at various locations on the power grid. This allows for much faster computer times because the algorithms contain magnitudes less data. When each distributed estimator completes its calculation, it sends the results back to the master device to check against all other distributed estimators.

Reen, DylanW.↗

DOE OSTI2023

Model-Free Probabilistic Forecasting of Nodal Voltages in Distribution Systems

As the penetration of distributed energy resources (DERs) into distribution systems increases, so does the interest in forecasting relevant system variables to help mitigate the associated challenges. One such challenge is the more frequent occurrence of excessive voltages in distribution systems with higher shares of DERs. Accurate and reliable estimates together with forecasts of system states (i.e., nodal voltages) will therefore play a key role in improving the utilization of these variable and uncertain sources while mitigating potential operational risks. Whilst recent literature has explored machine learning (ML) methods for voltage estimation and their extrapolation for a short-time period into the future, few have taken uncertainty quantification into account, and these methods have not yet been translated into operations. This paper discusses the advantages offered by probabilistic voltage forecasts and proposes a non-parametric Bayesian method suitable for forecasting nodal voltages at short-term time horizons while accounting for uncertainties in load and distributed photovoltaic (PV) generation. We demonstrate the value of the proposed Gaussian process (GP) model for a case study using historical forecasts and observation data.

distribution system↗

DOE OSTI2011

Distributed Dynamic State Estimation with Extended Kalman Filter

Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman filter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.

42 ENGINEERING↗

DOE OSTI2019

Application of the Koopman Operator-Theoretic Framework to Power System Dynamic State Estimation

Model-based and data-driven methods are combined to develop a hierarchical decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists of robust, model-based, decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data-driven DSE. The proposed hybrid framework does not depend on the centralized infrastructure of the control centers; thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator locations. Also, the network model is not necessary; thus, a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on the 2,000-bus synthetic Texas system and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.

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DOE OSTI2023

Modified Eigen-Decomposition-based Interval Analysis (MEDIA) for Power System Dynamic State Estimation

The Bayesian approach has been used for the dynamic state estimation (DSE) of a power system. However, due to the complexity of noise resources, it is difficult to quantify measurement and process noise using probability density functions (PDFs). To overcome the difficulty, the authors of this paper propose a modified eigen-decomposition-based interval analysis (MEDIA) method, which employs bounds instead of PDFs to quantify the noise, and uses the eigen decomposition method to reduce the negative impact of the overestimation problem. Using the simulation data generated from IEEE 16-machine and IEEE 10-machine systems, it is shown that the proposed MEDIA method can estimate the hard boundaries of dynamic states in real time. Furthermore, comparison with the forward-backward propagation method and the extended set-membership filter also shows that the proposed MEDIA method performs better by providing narrower boundaries in the DSE.

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