Primary Frequency Regulation with Load-Side Participation-Part I: Stability and Optimality

We present a method to design distributed generation and demand control schemes for primary frequency regulation in power networks that guarantee asymptotic stability and ensure fairness of allocation. We impose a passivity condition on net power supply variables and provide explicit steady state conditions on a general class of generation and demand control dynamics that ensure convergence of solutions to equilibria that solve an appropriately constructed network optimization problem. We also show that the inclusion of controllable demand results in a drop in steady state frequency deviations. We discuss how various classes of dynamics used in recent studies fit within our framework and show that this allows for less conservative stability and optimality conditions. We illustrate our results with simulations on the IEEE 68 bus system and observe that both static and dynamic demand response schemes that fit within our framework offer improved transient and steady state behavior compared with control of generation alone. The dynamic scheme is also seen to enhance the robustness of the system to time-delays.ER

Work In Progress - Multi-University Development And Dissemination Of Online Laboratories In Probability Theory, Signals And Systems, And Multimedia Computing

This collaborative effort involves five universities, namely, Arizona State University, the University of Washington-Bothell, the University of Texas at Dallas, the University of Rhode Island, and the University of Central Florida. The paper describes educational technology innovations and software extensions that enable the on-line software Java-DSP to be used in three courses at five different universities. The project includes educational innovations, software extensions to support on-line computer laboratories in four courses at five universities, and a dissemination and assessment plan. © 2005 IEEE

Efficient Modeling Of Dominant Transform Components Representing Time-Varying Signals

The mixed transform representation of time-varying signals uses partial sets of basis functions from the discrete Fourier transform (DFT) and the Walsh-Hadamard transform. The location, magnitude, and phase of the transform components have to be specified for proper signal reconstruction. A least-squares IIR (infinite impulse response) algorithm, in the transformed domains, which fits each of the retained subsets of the complex transform components accurately, is presented. The IIR function, while characterized by real coefficients about twice the number of the retained complex transform components, carries enough location, magnitude, and phase information for accurate signal reconstruction. To illustrate the technique\u27s accuracy and efficiency, its application to model the DFT part of a voice speech segment is given

Autoregressive Modeling and Feature Analysis of DNA Sequences

<p/> <p>A parametric signal processing approach for DNA sequence analysis based on autoregressive (AR) modeling is presented. AR model residual errors and AR model parameters are used as features. The AR residual error analysis indicate a high specificity of coding DNA sequences, while AR feature-based analysis helps distinguish between coding and noncoding DNA sequences. An AR model-based string searching algorithm is also proposed. The effect of several types of numerical mapping rules in th proposed method is demonstrated.</p

Error probability-based optimal training for linearly decoded orthogonal space-time block coded wireless systems

An optimal training strategy is devised for the linearly decoded orthogonal space-time block coded (OSTBC) wireless systems in quasi-static fading channel, based on the performance analysis using pairwise error probability (PEP) and symbol error probability (SEP). The PEP/SEP analyses allow us to find a generic expression for the performance improvement due to optimal training compared to the conventional case for OSTBC system equipped with any number of transmit and receive antennas and any linear modulation scheme. It is observed that the linear processing in the receiver, the most attractive feature of OSTBC, although destroys the orthogonality in the presence of channel estimation error, does not reduce diversity, but causes performance penalty as a loss of signal-to-noise ratio (LoSNR) due to training. This loss is quantified analytically and minimised by optimal allocation of power between training and data symbols. The performance of optimal power allocation improves with the higher number of space-time blocks in a frame. Furthermore, the LoSNR depends only on the OSTBC and is independent of any modulation scheme and the full rate Alamouti and other high rate OSTBCs suffer more in terms of performance due to training compared to the lower rate OSTBC

Measuring information flow in nonlinear systems--a modeling approach in the state space

Directional information flow between coupled nonlinear systems is of practical interest in many areas like bioengineering, chemistry, physics and electrical engineering. Due to the high complexity and nonlinearity of the coupled chaotic systems, linear modeling approaches may fail to capture the proper dynamics and thus the proper directional information flow. This necessitates novel approaches to analyze signals derived from such systems. This paper proposes a novel approach for detecting such directional information flows between the subsystems involved. The dependability of the method is illustrated using coupled chaotic oscillators in various coupling configurations

Energy management and modeling of a gridconnected BIPV system with battery energy storage

The increased penetration of renewables in power system generation has introduced several stability issues to existing power systems. Their intermittent nature in combination with the lack of rotational inertia made system operation even more difficult and frequency/voltage fluctuations larger. Storage systems have been identified as an ideal solution for mitigating these problems since their integration across the grid can reduce generation-load imbalances and assist in primary frequency regulation. Considering the importance storage systems have gained during the last years, in this paper we propose an energy management algorithm for a grid-connected PV system with battery storage. This model contains a Building Integrated Photovoltaic (BIPV) system connected to the grid through a DCDC boost converter, a DC-AC inverter and a battery storage system in active parallel configuration. Considering that the consumption of the building is satisfied from either the PV, the low voltage grid and/or the battery storage system, a specific energy management algorithm is presented for this model, in order to provide an efficient power flow between the aforementioned sources and the building load. The proposed model is implemented and verified through several simulations in Matlab/Simulink