Dagmar Niebur

Engineering the Future

A collaborative effort to strengthen the U.S. power and energy workforce. Some of us are old, some of us are young, and some of us refuse to acknowledge the difference. At any age, electric power and energy engineers contribute to the sustainability of life on this planet and the future growth of technology and society on all fronts. At a time when the U.S. economy is still struggling to employ more people, the power and energy sector worries about new talent to replace retiring experience. This article introduces readers to the Power and Energy Engineering Workforce Collaborative (PWC), an initiative on the part of IEEE Power & Energy Society (PES). The PWC was created to strengthen the U.S. power and energy workforce needed for the smart grid of the future and related technologies. Much of the material included here comes from the document shown in Figure 1. As these workforce issues greatly affect the United States, this work is being closely coordinated with IEEE-USA.

Load profile estimation in electric transmission networks using independent component analysis

It is important to estimate electric loads profiles in the deregulated environment where competing entities need to assess the load demands based on partial knowledge of the system. Independent component analysis (ICA) is a statistical technique used to separate linear mixtures of statistical independent source signals by maximization of negentropy. In this paper, we apply ICA to estimate load profiles using only a small set of active line flow measurements without prior knowledge of the electric network model parameters or topology. A filtering based preprocessing technique is used to ensure statistical independence of load components. The influence of measurement noise and nonlinearity of the power flow model are also investigated. The proposed approach is demonstrated for a five-bus system as well as the IEEE 30-bus system.

Harmonic Load Identification Using Complex Independent Component Analysis

Due to an increase of power-electronic equipment installation and other harmonic sources, the identification and estimation of harmonic loads are of concern in electric power transmission and distribution systems. Conventional harmonic state estimation requires a redundant number of expensive harmonic measurements. In this paper, we explore the use of a statistical signal-processing technique, known as independent component analysis (ICA) for harmonic source identification and estimation. If the harmonic currents are statistically independent, ICA is able to estimate the currents using a limited number of harmonic voltage measurements and without any knowledge of the system admittances or topology. The results are presented with computer simulations for the modified IEEE 30-bus test system.

Identification of capacitor position in a radial system

As modern equipment is more sensitive to power quality phenomena, transients introduced by capacitor switching become an increasing concern. The paper addresses the identification of switched capacitor position such that on-line or post-fault measures can be taken. The position is given in terms of proportional line impedance seen from the load bus. Perturbation methods are used to find an asymptotic expression for capacitor position as a function of the transient frequency. This paper presents analytical and simulation results for a standard radial distribution system.

DC Power Flow Based Contingency Analysis Using Graphics Processing Units

Graphic processing units (GPUs) are single instruction, multiple data processors which have become an integral part of modern high-end video cards installed on a general purpose PCs. This paper investigates the parallel implementation of DC power flow based contingency analysis on graphic processing units. Results for the IEEE standard test systems show a speed-up of the order of 4 that grows with the system size.

Evaluation of the Suitability of Different Chiller Performance Models for On-Line Training Applied to Automated Fault Detection and Diagnosis (RP-1139)

This paper presents the research results of comparing the suitability of four different chiller performance models to be used for on-line automated fault detection and diagnosis (FDD) of vapor-compression chillers. The models were limited to steady-state performance and included (a) black-box multivariate polynomial (MP) models; (b) artificial neural network (ANN) models, specifically radial basis function (RBF) and multilayer perceptron (MLP); (c) the generic physical component (PC) model approach; and (d) the lumped physical Gordon-Ng (GN) model. All models except for (b) are linear in the parameters. A review of the engineering literature identified the three following on-line training schemes as suitable for evaluation: ordinary recursive least squares (ORLS) under incremental window scheme, sliding window scheme, and weighted recursive least squares (WRLS) scheme, where more weight is given to newer data. The evaluation was done based on five months of data from a 220 ton field-operated chiller from Toronto (a data set of 810 data points) and fourteen days of data from a 450 ton field-operated chiller (a set of about 1120 data points) located on Drexel University campus. The evaluation included a preliminary off-line or batch analysis to gain a first understanding of the suitability of the various models and their particular drawbacks and then to investigate whether the different chiller models exhibit any time variant or seasonal behavior. The subsequent on-line evaluation consisted of assessing the various models in terms of their suitability for model parameter tracking as well as model prediction accuracy (which would provide the necessary thresholds for flagging occurrence of faults). The former assessment suggested that parameter tracking using the GN model parameters could be a viable option for fault detection (FD) implementation, while the black box models were not at all suitable given their high standard errors. The assessment of models in terms of their internal prediction accuracy revealed that the MLP model was best, followed by the MP and GN models. However, the more important test of external predictive accuracy suggests that all models are equally accurate (CV about 2% to 4%) and, hence, comparable within the experimental uncertainty of the data. ORLS with incremental window scheme was found to be the most robust compared to the other computational schemes. The chiller models do not exhibit any time variant behavior since WRLS was found to be poorest. Finally, in terms of the initial length of training data, it was determined—at least with the data sets used that exhibited high autocorrelation—that about 320 and 400 data points would be respectively necessary for the MP and GN model parameter estimates to stabilize at their long-term values. This paper also provides a detailed discussion of the potential advantages that on-line model training can offer and identifies areas of follow-up research.

Simulink Model for Hybrid Power System Test-bed

The Hybrid Power System Test-bed currently being constructed at NAVSEA Philadelphia is designed to serve as a developmental platform for the evaluation of hybrid power and propulsion options related to unmanned surface vehicles (USVs). Prior to component integration, a Matlab/Simulink model of the test-bed was created to simulate the system interactions and assist in the detailed electrical design. A reduced order system model was required before a detailed analysis of the individual components was performed. This paper describes the evolution of the test-bed model and the work to date in developing detailed component models for the system.

Three-phase converter models for unbalanced radial power-flow studies

This paper presents a power converter model intended for both balanced and unbalanced radial power flow studies. A three-phase steady state model of a power converter (ac/dc-dc/ac) composed of a diode rectifier, a loss-less dc link, and a pulse-width-modulated inverter is presented. Both the three-phase rectifier and the three-phase inverter are modeled as three equivalent Y-connected single-phase rectifiers and single-phase inverters, respectively. The model is implemented within a three-phase power flow solver with phase representation. Simulation results on a balanced and an unbalanced 15-bus system are presented.

Multiple power flow solutions of small integrated AC/DC power systems

To study the steady state behavior of small integrated AC/DC power systems, a new realization of AC equivalent approach to AC/DC power flow is proposed in this paper. Depending on control modes, the converter AC terminal buses are modeled as constant voltage bus (slack bus) and nonlinear load bus (PI bus). As a result the whole system and corresponding Jacobian matrix become area-decoupled. In this paper we study two control modes through analysis of multiple load flow solutions thus providing a quantitative and qualitative assessment of feasible and controllable system operating points.

OPTIMAL POWER SYSTEM MANAGEMENT VIA MIXED INTEGER DYNAMIC PROGRAMMING

Abstract Power systems involve both continuous and discrete acting components and subsystems. In this work a logical specification is used to define the transition dynamics of the discrete subsystem. A computational tool that reduces the logical specification to a set of inequalities and the use of the transformed model in a dynamic programming approach to the design of optimal feedback controls arc described. An example of optimal load shedding for a power system with aggregate induction motor and constant admittance loads is given.