skip to main content

This content will become publicly available on December 1, 2022

Title: μPMU-Based Temporal Decoupling of Parameter and Measurement Gross Error Processing in DSSE
Simultaneous real-time monitoring of measurement and parameter gross errors poses a great challenge to distribution system state estimation due to usually low measurement redundancy. This paper presents a gross error analysis framework, employing μPMUs to decouple the error analysis of measurements and parameters. When a recent measurement scan from SCADA RTUs and smart meters is available, gross error analysis of measurements is performed as a post-processing step of non-linear DSSE (NLSE). In between scans of SCADA and AMI measurements, a linear state estimator (LSE) using μPMU measurements and linearized SCADA and AMI measurements is used to detect parameter data changes caused by the operation of Volt/Var controls. For every execution of the LSE, the variance of the unsynchronized measurements is updated according to the uncertainty introduced by load dynamics, which are modeled as an Ornstein–Uhlenbeck random process. The update of variance of unsynchronized measurements can avoid the wrong detection of errors and can model the trustworthiness of outdated or obsolete data. When new SCADA and AMI measurements arrive, the LSE provides added redundancy to the NLSE through synthetic measurements. The presented framework was tested on a 13-bus test system. Test results highlight that the LSE and NLSE processes successfully work more » together to analyze bad data for both measurements and parameters. « less
Authors:
; ; ; ; ;
Award ID(s):
1809739
Publication Date:
NSF-PAR ID:
10343775
Journal Name:
Electricity
Volume:
2
Issue:
4
Page Range or eLocation-ID:
423 to 438
ISSN:
2673-4826
Sponsoring Org:
National Science Foundation
More Like this
  1. Cassio de Campos ; Marloes H. Maathuis (Ed.)
    When data contains measurement errors, it is necessary to make modeling assumptions relating the error-prone measurements to the unobserved true values. Work on measurement error has largely focused on models that fully identify the parameter of interest. As a result, many practically useful models that result in bounds on the target parameter -- known as partial identification -- have been neglected. In this work, we present a method for partial identification in a class of measurement error models involving discrete variables. We focus on models that impose linear constraints on the tar- get parameter, allowing us to compute partial identification bounds using off-the-shelf LP solvers. We show how several common measurement error assumptions can be composed with an extended class of instrumental variable-type models to create such linear constraint sets. We further show how this approach can be used to bound causal parameters, such as the average treatment effect, when treatment or outcome variables are measured with error. Using data from the Oregon Health Insurance Experiment, we apply this method to estimate bounds on the effect Medicaid enrollment has on depression when depression is measured with error.
  2. Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed onmore »the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.« less
  3. The paper explores the effects of sensor behavior and communication system (CS) irregularities on power system state estimation (SE). CS are modeled in Network Simulator 2 (NS-2), allowing the quantification of irregularities, including delays and dropped packets. The overall information is obtained combining SCADA measurements with phasor measurement unit (PMU) derived data, where time stamping (based on GPS or an equivalent local clock) for all measurements is assumed. To fully analyze the effects of irregularities, a detailed analysis of sensitivities to different communication system parameters is provided as well. Using the co-simulation environment PiccSIM, a SE with these irregularities is quantified for CS parameter variation, with detailed models of power and communication flows.
  4. This paper explores power system network observability while taking into account realistic communication network behavior. The overall information is obtained by combining SCADA- and phasor measurement unit-derived data, where time stamping (based on Global Positioning System or an equivalent local clock) for all measurements is assumed. Based on simulations performed in communication Network Simulator 2, empirical cumulative distribution functions can be associated with transfer times of measurement packets, which will reflect communication parameters and irregularities. This is further used to form an algorithm which maximizes the number of successful network observability checks, and thus the number of possible state estimations, in a certain time period. Application is demonstrated on the IEEE 14-bus test power system example.
  5. In the modern power system networks, grid observability has greatly increased due to the deployment of various metering technologies. Such technologies enhanced the real-time monitoring of the grid. The collection of observations are processed by the state estimator in which many applications have relied on. Traditionally, state estimation on power grids has been done considering a centralized architecture. With grid deregulation, and awareness of information privacy and security, much attention has been given to multi-area state estimation. Considering such, state-of-the-art solutions consider a weighted norm of residual measurement model, which might hinder masked gross errors contained in the null-space of the Jacobian matrix. Towards the solution of this, a distributed innovation-based model is presented. Measurement innovation is used towards error composition. The measurement error is an independent random variable, where the residual is not. Thus, the masked component is recovered through measurement innovation. Model solution is obtained through an Alternating Direction Method of Multipliers (ADMM), which requires minimal information communication. The presented framework is validated using the IEEE 14 and IEEE 118 bus systems. Easy-to-implement model, build-on the classical weighted norm of the residual solution, and without hard-to-design parameters highlight potential aspects towards real-life implementation.