F. El-Hawary

A summary of environmental/economic dispatch algorithms

Traditionally electric power systems are operated in such a way that the total fuel cost is minimized regardless of emissions produced. With increased requirements for environmental protection, alternative strategies are required. This paper presents a summary of algorithms of environmental-economic dispatch in electric power systems since 1970. The algorithms attempt to reduce the production of atmospheric emissions such as NO/sub x/ and SO/sub x/ caused by the operation of fossil-fueled thermal generation. Such reduction is achieved by including emissions either as a constraint or as a weighted function the objective of the overall dispatching problem. >

Optimal environmental dispatching of electric power systems via an improved Hopfield neural network model

This paper examines the usefulness and specifics of the Hopfield neural network as applied to optimal economic/environmental dispatching of thermal generating units in an electric power system. A simulator is developed and criteria for selecting its parameters are discussed to obtain an optimal dispatch in the minimum amount of iterations. Particular emphasis is placed on the effects of variations in the selection of Hopfield parameters both with respect to the final result and the number of iterations required to reach a final solution. >

Minimum emissions power flow

A formulation of the optimal power flow problem to include the minimum emission objective is presented in this paper. Fuel cost and emissions such as NO/sub x/ objectives are included in the formulation. Emissions can also be included as constraints. The trade-off relation between fuel cost and emissions is also studied in this paper. Optimizing the objective for a whole period of time of different time intervals and system demands while satisfying a maximum limit on a constraint, such as the total emissions produced in the whole period while minimizing fuel cost, or the total fuel cost while minimizing emissions, is also covered in the study. >

Robust regression-based EKF for tracking underwater targets

In underwater target tracking applications, measurement uncertainty and inaccuracies are usually modeled as additive Gaussian noise. The Gaussian model of noise may not be appropriate in many practical systems. The non-Gaussian noise and the model non-linearity arising in a tracking system will seriously affect the tracking performance. This paper discusses one way to create a robust version of the extended Kalman filter for enhanced underwater target tracking. State estimation in the filter is done through the robust regression approach and Welschs proposal is used in the regression process. Monte Carlo simulation results with heavy-tailed contaminated observation noise demonstrate the robustness of the proposed estimation procedure. >

Spatial parameter estimation for ocean subsurface layered media

Discusses the problem of obtaining estimates of amplitude and delay parameters associated with the response to impulsive acoustic excitations of the ocean subbottom. The availability of the estimates is important for identifying properties and geometry of underwater sediments and rock formations. The authors detail the basic model for ocean bottom subsurface reflections and gives the mathematical basis for extraction of parameter estimates for the model by a correlation method. Some results are presented for estimates on field data. It is found that for the data records available the results after estimation only contain false events and unrecognized events. Spatial filtering and smoothing is suggested to improve estimates and their reliability.

A sensitivity analysis approach to minimum emissions power flow

This paper describes a method to obtain a new state from an optimal state for not too large a change in parameters that change very frequently such as system load using sensitivity analysis. When the system load changes, cost and emissions also change to values which depend on the objective function to be minimized and in turn on the weighting factors assigned to each function in the case of multi-objective optimization. This requires a new optimal solution for any change in system load. A dispatcher may need to change a weighting factor in order to reduce emissions or cost from one case to another which requires a complete solution of the problem any time a change takes place. However, if the changes in system loading are not too large or a small change in a weighting factor or emissions constraint is needed, then a sensitivity analysis may be used without losing significant accuracy to change the state of the system in real time which has the advantage of requiring less time than a complete optimal solution. >

The generalized Kalman filter approach to adaptive underwater target tracking

The generalized Kalman filtering (GKF) method is applied to underwater target tracking. The proposed GKF is based on the formulation developed by J.C. Lagarias and F. Aminzadeh (1983) establishing a tradeoff between the cost associated with estimation error and the cost related to the lateral discontinuity of the estimates. By assigning proper weights for accuracy and stability in the objective function, the desired balance between accuracy of estimates and lateral continuity is achieved. Computational results illustrate the performance of the technique. Conclusions as to the effects of the accuracy and stability weights are drawn. >

Compensation for source heave by use of a Kalman filter

This paper presents a procedure for data filtering to compensate for the effects of the towed body dynamics (heave), in shallow marine seismic reflection records. A method to extract an approximate record of the heave contribution to data collected is outlined. The method utilizes the time to water-sediment interface on each acoustic return record to construct the required approximate heave motion record. The frequency response of the heave component record provides the basis for a proposed linear model for the heave motion. A formulation of the heave compensation requirement as a Kalman filtering problem in optimal linear estimation theory is given. A discussion of the computational aspects and practical results are discussed to conclude the paper.

Event enhancement of reflections from subsurface layered media

The problem of obtaining amplitude and delay parameters associated with the response of subsurface layered sedimentation to impulse-type acoustic excitations is considered. We use a linear lossless model which characterizes the subsurface in terms of layerwise homogeneous segments. The parameters of the model are the time delays associated with the wave propagation in the various layers and amplitude parameters which are functions of the reflection coefficients at the interfaces. With knowledge of the travel time in a sediment of rock layer, its thickness can be estimated if the medium velocity is known. This paper extends theory developed by the authors. In particular, a procedure to enhance the detection of reflection events is presented. The procedure utilizes a balance property shown by the derivative of the input waveform signal to obtain an improved display of reflection-event portions of the received signals. Computational results are given in the paper to illustrate the effectivess of the procedure.

Hybrid adaptive control of autonomous underwater vehicle

Hybrid adaptive control of autonomous underwater vehicle (AUV) is investigated. Dynamics of AUV vary by change in operating conditions and even theoretically or experimentally driven dynamical coefficients reflect an approximate to the exact ones. Adaptive control technique is employed to handle the uncertainty problems in the system dynamics. In the applied hybrid adaptive control, the system is simulated in a continuous domain while the control and identification sections are discrete. The discrete model and position of zeros of sampled data unstable system are addressed. Convergence rate of parameter estimation is crucial in the stability of closed loop system particularly when open loop unstable system passes its initial states or is entangled by radical changes in the dynamics. Adaptive normalization is suggested which improves the rate of convergence and conserves stability. The results of modified direct, indirect and linear quadratic Gaussian (LQG) adaptive control are presented.