Thomas A. Ferryman

Uncertainty quantification in state estimation using the probabilistic collocation method

This paper proposes a probabilistic collocation method (PCM) to quantify the uncertainties in state estimation. Comparing to classic Monte-Carlo (MC) method, the proposed PCM is based on sparse grid points and uses a smaller number of sparse grid points to quantify the uncertainty. Thus, the proposed PCM can quantify a large number of uncertain power system variables with relatively lower computational cost. The algorithm and procedure are outlined. The proposed PCM is applied to IEEE 14 bus system to quantify the uncertainty of power system state estimation. Comparison is made with MC method. The simulation results shows that the proposed PCM can achieve same accuracy as MC method with smaller ensemble size and thus is computationally more efficient than MC method.

Net interchange schedule forecasting of electric power exchange for RTO/ISOs

Neighboring regional transmission organizations (RTO) and independent system operators (ISOs) exchange electric power to enable efficient and reliable operation of the grid. Net interchange (NI) schedule is the sum of the transactions (in MW) between an RTO/ISO and its neighbors. Effective forecasting of the amount of actual NI can improve grid operation efficiency and avoid the volatility of the energy markets due to changes of NI schedules. This paper presents results of a preliminary investigation into various methods of prediction that may result in improved prediction accuracy. The methods studied are linear regression, forward regression, stepwise regression, and support vector machine (SVM) regression. The effectiveness of these methods is compared using the 64 weeks of field measurement data from PJM. The objective is to explore the effectiveness of the prediction methods under different scenarios.

Statistical Analysis of Abnormal Electric Power Grid Behavior

Pacific Northwest National Laboratory is developing a technique to analyze Phasor Measurement Unit data to identify typical patterns, atypical events, and precursors to a blackout or other undesirable event. The technique combines data-driven multivariate analyses and engineering-modeling. The method identifies atypical events, provides a plain English description of the event, and gives the capability to use drill-down graphics for detailed investigations. The tool can be applied to the entire grid, individual organizations (e.g., TVA, BPA), or specific substations (e.g., TVA_CUMB). The tool is envisioned for (1) event investigations, (2) overnight processing to generate a Morning Report that characterizes activity that has occurred during the previous 10-30 days, and (3) potentially near-real-time operation to support the grid operators.This paper presents the current status of the tool and illustrations of its application to real world PMU data collected in three 10-day periods in 2007.

Investigation of Phase Angle Differences Using Statistical Analysis of Real World State Estimator Data

Phase angle differences may provide very useful insight to guide the operations of the electric power grid. This analysis investigates two different methods to use data-driven statistical analysis methods to identify normal patterns and atypical events based on State Estimator phase angel differences. An ISO provided 15 months of State Estimator data. This data enabled the calculation of phase angle differences for 54 pairs of sites. The identified atypical events for both methods were compared to the ISOs operations log.

An ensemble approach for forecasting net interchange schedule

The net interchange schedule (NIS) is the sum of the transactions (MW) between an Independent System Operator/Regional Transmission Organization (ISO/RTO) and its neighbors. Effective forecasting of the submitted NIS can improve grid operation efficiency. This paper applies a Bayesian model averaging (BMA) technique to forecast submitted NIS. As an ensemble approach, the BMA method aggregates different forecasting models in order to improve forecasting accuracy and consistency. In this study, the BMA method is compared to two alternative approaches: a stepwise regression method and an artificial neural network (ANN) trained for NIS forecasting. In our comparative analysis, we use field measurement data from the PJM Interconnection RTO to train and test each method. Our preliminary results indicate that ensemble-based methods can provide more accurate and consistent NIS forecasts in comparison to non-ensemble alternate methods.

Initial study on the predictability of real power on the grid based on PMU data

Operations on the electric power grid provide highly reliable power to the end users. These operations involve hundreds of human operators and automated control schemes. However, the operations process can often take several minutes to complete. During these several minutes, the operations are often evaluated on a past state of the power system. Proper prediction methods could change this to make the operations evaluate the state of the power grid minutes in advance. Such information allows proactive, rather than reactive, actions on the power system and aids in improving the efficiency and reliability of the power grid as a whole. A successful demonstration of this prediction framework is necessary to evaluate the feasibility of utilizing such predicted states in grid operations.

Typical Patterns, Atypical Events, and Uncertainty in Complex Systems

The power grid is a complex system. Multiple quantities are measured from hundreds of locations, at rates up to 30 Hz. There are both correlated and uncorrelated variables. Powerful methods are needed to examine this large amount of data and better understand the complex system, and in the case of the power grid, identify imminent adverse events, such as blackouts. These methods need to sift through any multicollinearity among the variables, account for the random uncertainty that is present within each variable, and focus on practical differences as defined by domain experts in addition to statistical differences. These methods will then help the user to better understand the complex system by uncovering the hidden gems within the data. These gems include identification of the uncertainty, characterization of the typical patterns, and the discovery of atypical events. This paper will discuss the intricate methods used to explore the data, and the novel displays used to communicate the findings. This paper will also delve into the exploration of other complex systems, like aviation safety, using similar methods.Copyright

Uncertainty estimation of Hanford tank chemical and radionuclide inventories and concentrations

The exact physical and chemical nature of 55 million gallons of radioactive toxic waste held in 177 underground waste tanks at the Hanford Site is not known with sufficient detail to support the safety, retrieval, and immobilization missions presented to Hanford. The purpose of this study is to estimate probability distributions for the inventory of each of 72 analytes in each of 177 tanks. This will enable uncertainty intervals to be calculated for inventories and should facilitate the safety, retrieval, and immobilization missions.