Richard W. Wies

Simulink model for economic analysis and environmental impacts of a PV with diesel-battery system for remote villages

This paper discusses the economic analysis and environmental impacts of integrating a photovoltaic (PV) array into diesel-electric power systems for remote villages. MATLAB Simulink is used to match the load with the demand and apportion the electrical production between the PV and diesel-electric generator. The economic part of the model calculates the fuel consumed, the kilowatthours obtained per gallon of fuel supplied, and the total cost of fuel. The environmental part of the model calculates the CO/sub 2/, particulate matter (PM), and the NO/sub x/ emitted to the atmosphere. Simulations based on an actual system in the remote Alaskan community of Lime Village were performed for three cases: 1) diesel only; 2) diesel-battery; and 3) PV with diesel-battery using a one-year time period. The simulation results were utilized to calculate the energy payback, the simple payback time for the PV module, and the avoided costs of CO/sub 2/, NO/sub x/, and PM. Post-simulation analysis includes the comparison of results with those predicted by Hybrid Optimization Model for Electric Renewables (HOMER). The life-cycle cost (LCC) and air emissions results of our Simulink model were comparable to those predicted by HOMER.

Use of ARMA block processing for estimating stationary low-frequency electromechanical modes of power systems

Accurate knowledge of low-frequency electromechanical modes in power systems gives vital information about the stability of the system. Current techniques for estimating electromechanical modes are computationally intensive and rely on complex system models. This research complements model-based approaches and uses measurement-based techniques. This paper discusses the development of an auto-regressive moving average (ARMA) block processing technique to estimate these low-frequency electromechanical modes from measured ambient power system data without requiring a disturbance. This technique is applied to simulated data containing a stationary low-frequency mode generated from a 19-machine test model. The frequency and damping factor of the estimated modes are compared with the actual modes for various block sizes. This technique is also applied to 35-minute blocks of actual ambient power system data before and after a disturbance and compared to results from Prony analysis on the ringdown from the disturbance.

Bootstrap-based confidence interval estimates for electromechanical modes from multiple output analysis of measured ambient data

Previously, variations of the Yule-Walker techniques have been applied successfully to give point estimates of electromechanical modes of a power system based on measured ambient data. This paper introduces a bootstrap method to give confidence interval estimates for the electromechanical modes. Simulation results from a 19-machine model show the validation of the bootstrap method and its consistence to Monte Carlo methods. Actual measurement data taken from western North American Power Grid in 2000 are processed using the bootstrap method to give confidence interval estimates for interarea mode damping ratios. The use of multiple outputs is shown to improve the performance and tighten the confidence intervals.

Combining least mean squares adaptive filter and auto-regressive block processing techniques for estimating the low-frequency electromechanical modes in power systems

A variety of techniques have been developed for estimating the low-frequency electromechanical modes of power systems based on the analysis of complex system models, ring downs from a system disturbance, and noise injection signals. This work uses a combination of adaptive filtering and block processing techniques to estimate these modes from ambient power system data. Current methods of using these techniques separately have seen constraints arising with the convergence time and variability of the estimates for adaptive algorithms and the large blocks of data required for block processing. This paper investigates possible ways of overcoming these constraints by evaluating the performance of the least mean squares (LMS) adaptive filtering algorithm, taking into consideration the step size parameter (mu) and the initial weight vector estimate from auto regressive (AR) block processing. This technique is applied to simulated data containing a stationary low-frequency mode generated from a 19-machine test system and to actual ambient power system data recorded from the western North American power grid in June 2000. The frequency and damping factor estimates from LMS alone and in combination with AR are compared with the actual modes (test system) and with over-determined AR (full block of ambient data) to measure the improvement in convergence time and the variability of the estimates

Using Adaptive Step-Size Least Mean Squares (ASLMS) for Estimating Low-Frequency Electromechanical Modes in Power Systems

Information about the stability of heavily interconnected power systems is found in low-frequency electromechanical modes. This research complements model-based and measurement-based approaches to mode estimation typically requiring much computation and ringdown from a disturbance, respectively. This paper discusses an adaptive step-size least mean square algorithm (ASLMS) for estimating the electromechanical modes near real time in which the step size (mu) is adaptively controlled to improve the convergence time of the estimates compared to the LMS. A simple gradient vector (gamma) and a small positive constant (rho) are introduced for controlled adaptation of mu. The improvement in convergence performance of the frequency and damping estimates when compared to the LMS estimates is shown by applying the ASLMS to simulated data containing a stationary mode from a 19-machine test model and ambient power system data, while using an initial weight vector from previous ASLMS, LMS and block processing algorithm estimates

Adaptive Filtering Techniques for Estimating Electromechanical Modes in Power Systems

Previous adaptive filtering algorithms using the least mean square (LMS) technique for estimating electromechanical modes in power systems imposed constraints arising from the variability and time of convergence of the filter estimates. Also these techniques assumed the power system data to be wide-sense stationary. This work presents a combination of adaptive filtering and block processing algorithms to overcome the constraints of variability and time of convergence of the mode estimates. This work also introduces an adaptive step size least mean squares (ASLMS) algorithm assuming the non-stationary nature of power system data. Finally, this paper investigates the use of an error tracking (ET) algorithm, a combination of LMS and ASLMS algorithms based on the estimation error of the adaptive filters. These techniques are applied to two sets of actual power system data recorded from the western North American power grid. The first data set recorded in June 2000 is 18.85 minutes long, and the second data set recorded in May 2005 is 180 minutes long. The June 2000 data has a lower damping ratio and is more non- stationary than the May 2005 data set in a signal processing sense. The results show that using the LMS in combination with the ASLMS algorithm designed to work in non-stationary environments reduces the variability of the mode estimates and the time of convergence.

Nonlinear

Wind turbine technology has evolved into a unique technical identity with potential to contribute significantly to the global energy mix powered by renewables. Wind energy, being a fluctuating resource, requires tight control management that ad dresses stability issues for it to be integrable with the grid system. Difficulty in controller construction for wind energy conversion systems (WECSs) is reinforced by sensitivity to numerical complexity, fast parameter variations due to wind stochasticity, tight performance requirements, as well as the presence of flexible modes that limit the control bandwidth. Proposed herein is a pitch control scheme and a model-based H∞ synthesis controller that yields a multivariable control law governing operation of the power electronic converter for a megawatt-class WECS over the entire nominal operating trajectory. The H∞ control objectives are cast as optimization programs with a unique cost function subject to linear matrix inequality constraints. Simulational analysis confirms the efficacy of the adopted technique: issues regarding uncertain ties with respect to system modeling and possible adverse control due to interactions with highly turbulent winds are handled with precision, while significantly improving the quality of voltage and output power.

{\cal H}_{\infty }

Isolated hybrid wind microgrids operate within three distinct modes, depending on the wind resources and the consumer grid demand: diesel-only (DO), wind-diesel (WD) and windonly (WO). Few successful systems have been shown to consistently and smoothly transition between wind-diesel and wind-only modes. The University of Alaska – Fairbanks Alaska Center for Energy and Power (ACEP) has constructed a full scale test bed of such a system in order to evaluate technologies that facilitate this transition. The test bed is similar in design to the NREL Power Systems Integration Laboratory (PSIL) and sized to represent a typical off-grid community. The objective of the present work is to model the ACEP test bed in DO and WD modes using MATLAB™ SIMULINK

Constrained Feedback Control for Grid-Interactive WECS Under High Stochasticity

The integration of electric machines and drive systems into Unmanned Ground Vehicle (UGV) applications depends largely on meeting requirements of drive power and speed control with the limited space of an in-wheel design. In addition, UGV drive systems must operate efficiently under all conditions so as to minimize the power consumption from limited power source. The concern for energy consumption and space limitations in UGV applications suggests the need for application specific motors and control systems that are integral part of the vehicle design. The performance of a design specific electric motor and control system for a UGV application in a simulated environment and on a laboratory test bench would provide much information about the motors operating parameters and allow for optimization of the drive system for the specific UGV application. The parameters of concern here are the output power and torque of the motor over the speed range of interest and the overall efficiency of the drive system. The effects of speed control algorithms on motor performance are also of importance in determining the capabilities of the motor and control system as an integrated unit. This paper presents the development and initial testing of an integrated UGV drive system in the Power Electronics Lab (PEL) at the University of Alaska Fairbanks (UAF) in a joint effort with Utah State University (USU). The UGV drive system employs a custom designed axial-gap permanent magnet synchronous motor (AGPMSM) with scalar control.

DEVELOPMENT OF A FULL-SCALE-LAB-VALIDATED DYNAMIC SIMULINK © MODEL FOR A STAND-ALONE WIND-POWERED MICROGRID

Frequency regulation in wind-powered islanded microgrids (WPIM) is critical for system stability given unpredictable dynamics from variations in wind generation and demand. Traditional methods of frequency regulation in WPIM have used classical secondary load controllers (CSLC) in a centralized approach to buffer wind generation and demand events. This study investigates the feasibility of using a network of self-sensing distributed secondary loads (SSDSL) consisting of electric-thermal storage (ETS) to assist in frequency regulation in WPIM. Individual SSDSL sense the local grid frequency and activate resistive load elements in order to absorb surplus energy during high wind events. Four major parameters: 1) zero-order hold time 2) full response point 3) network capacity ratio, and 4) coordination mode, are used in a dynamic model to explore the effect of SSDSL on frequency regulation. SSDSL are shown to assist with frequency regulation in WPIM.