Miodrag Djukanovic

Sequential synthesis of structured singular value based decentralized controllers in power systems

This paper presents a systematic procedure for the design of decentralized controllers for multimachine power systems. The robust performance in terms of the structured singular value (SSV or /spl mu/) is used as the measure of control performance. A wide range of operating conditions were used for testing. Simulation results have shown that the resulting /spl mu/- controllers would effectively enhance the damping torques, providing better robust stability and/or performance characteristics both in the frequency and time-domain compared to conventionally designed power system stabilizers (PSSs).

Application of the structured singular value theory for robust stability and control analysis in multimachine power systems. I. Framework development

In this paper a framework for robust stability assessment of controls in multimachine power systems is proposed. Variations in the operating conditions are modeled and then incorporated into a framework amenable for robust stability analysis. The structured singular value (SSV) tool is then applied in order to determine the stability for given variations in the operating conditions.

Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order nonlinear hydrogenerator model.

Neural-net based coordinated stabilizing control for the exciter and governor loops of low head hydropower plants

This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbines are performed and the comparisons of conventional excitation-governor state-space optimal control and neural-net based control are presented. Results obtained on the nonlinear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers.

Sensitivity based structured singular value approach to stability robustness of power systems

This paper presents a framework for robust stability assessment in multimachine power systems using first and second order sensitivities of the elements of the system A-matrix with respect to variations of a specified uncertain parameter. Variations in the mechanical power of critical generators are modeled and then incorporated into a framework amenable for robust stability analysis. The proposed model is then incorporated into a previously developed structured singular value (SSV) based framework in order to determine the stability of power systems for given variations in the operating conditions caused by the parameter uncertainty. Simulation results on the IEEE 50 generator system have shown that the proposed method significantly reduces computational complexity and at the same time preserves the accuracy in predicting stability robustness.

Neural-net based real-time economic dispatch for thermal power plants

This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal power plant units. The approach can take into account operational requirements and power network losses. The proposed economic dispatch uses an artificial neural network (ANN) for the generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal power systems, based on neural net theory for simplified solution algorithms and an improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories by applying neural net forecasts of power system load patterns.

Coordinated stabilizing control for the exciter and governor loops using fuzzy set theory and neural nets

Abstract This paper presents a design technique for a new hydropower plant controller using fuzzy set theory and artificial neural networks. The controller is suitable for real time operation, with the aim of improving the generating unit transients by acting through the exciter input, the guide vane and the runner blade positions. The developed fuzzy logic based controller (FLC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wider range of operating conditions than conventional regulators. Digital simulations of a hydropower plant equipped with a low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, optimal state-feedback control and FLC performances are presented. The FLC, based on a set of fuzzy logic operations that are performed on controller inputs, provides a means of converting linguistic control requirements based on expert knowledge into an efficient control strategy. A fuzzy associative matrix is generated by using unsupervised learning of artificial neural networks. Results obtained on the nonlinear hydrounit mathematical model simulation demonstrate that the performance of the FLC closely agrees with that obtained if the optimal state-feedback multivariable discrete-time controller is applied.

Artificial neural network based identification of dynamic equivalents

Abstract This paper presents techniques for identifying coherent generators using artificial neural networks. The proposed method is used to identify three types of equivalents, parochial, local and global. Identification is based on the adaptive pattern recognition concept and four features which are considered to be central to the phenomenon of coherency. An algorithm based on an algebraic characterization of global equivalents using an inertially weighted synchronizing torque matrix is proposed. The method is applied to the well-known 39-bus New England system, taken as a test example.

A neural-net based short term load forecasting using moving window procedure

Abstract An improved neural-net approach based on a combined unsupervised/supervised learning concept is proposed. A ‘moving window’ procedure is applied to the most recent load and weather information for creating training set data base. A forecasting lead time that varies from 16 hours to 88 hours is introduced to produce the short term electric load forecasting that meets requirements of real electric utility operating practice. The unsupervised learning (UL) is used to identify days with similar daily load patterns. A feed forward three-layer neural net is designed to predict 24-hour loads within the supervised learning (SL) phase. The effectiveness of proposed methods is demonstrated by comparison of forecasted hourly loads in every single day during 1991 with data realized in the same period in the Electric Power Utility of Serbia (EPS). A better choice of input features and more appropriate training set selection procedure allow significant improvement in forecasting results comparing with our previous UL/SL concept characterized by a fixed neural-net structure and absence of re-training procedure. The improvement is illustrated by reduction of average error in daily energy forecasting for 0.83% and reduction of 90th percentile of 2.04%.

Fuzzy linear programming based optimal fuel scheduling incorporating blending/transloading facilities

In this paper, power system blending/transloading facilities are modeled using an interactive fuzzy linear programming (FLP), in order to allow the decision-maker to solve the problem of uncertainty of input information within the fuel scheduling optimization. An interactive decision-making process is formulated in which decision-maker can learn to recognize good solutions by considering all possibilities of fuzziness. The application of the fuzzy formulation is accompanied by a careful examination of the definition of fuzziness, appropriateness of the membership function and interpretation of results. The proposed concept provides a decision support system with integration-oriented features, whereby the decision-maker can learn to recognize the relative importance of factors in the specific domain of optimal fuel scheduling (OFS) problem. The formulation of a fuzzy linear programming problem to obtain a reasonable nonfuzzy solution under consideration of the ambiguity of parameters, represented by fuzzy numbers, is introduced. An additional advantage of the FLP formulation is its ability to deal with multi-objective problems.