A general study of the transient stability of synchronous machines

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Practical Issues Regarding the Use of Dielectric Measurements to Diagnose the Service Health of MV Cables

Presented at Jicable '07.During the last decade, Very Low Frequency (VLF) testing for extruded distribution cables has gained interest among the North American utilities. The increasing interest is evidenced by recent research publications and discussions inside the expert community in which standards are being proposed and continuously discussed. While there is a general consensus as to the meaning of insulation dielectric properties, many open issues still remain for discussion in order to produce a more accurate evaluation. Consequently, this paper will discuss a number of the practical issues that arise when making these measurements at VLF on field aged and non-aged cables, particularly Tan δ measurements. The discussion is based on data from laboratory experiments and field testing.The work reported here was supported by a large number of utilities in North America and the U.S. Department of Energy under award number DE-FC02-04CH11237

Two Separate Continually Online-Trained Neurocontrollers for Excitation and Turbine Control of a Turbogenerator

This paper presents the design of two separate continually online trained (COT) neurocontrollers for excitation and turbine control of a turbogenerator connected to the infinite bus through a transmission line. These neurocontrollers augment/replace the conventional automatic voltage regulator and the turbine governor of a generator. A third COT artificial neural network is used to identify the complex nonlinear dynamics of the power system. Results are presented to show that the two COT neurocontrollers can control turbogenerators under steady-state as well as transient conditions and, thus, allow turbogenerators to operate more closely to their steady-state stability limit

Adaptive Critic Designs for Optimal Control of Power Systems

The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation, turbine and flexible AC transmission systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and realtime laboratory experimental studies carried out are described and the results show the successful control of the power system elements and the entire power system with adaptive and optimal neurocontrol schemes. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances

Swarm Intelligence for Transmission System Control

Many areas related to power system transmission require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics based swarm intelligence can be an efficient alternative. This paper highlights the application of swam intelligence techniques for solving some of the transmission system control problems

Intelligent Optimal Control of Excitation and Turbine Systems in Power Networks

The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation and turbine systems. The crucial factors affecting the modern power systems today is voltage control and system stabilization during small and large disturbances. Simulation studies and real-time laboratory experimental studies carried out are described and the results show the successful control of the power system excitation and turbine systems with adaptive and optimal neurocontrol approaches. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances

Neural network for estimating and compensating the nonlinear characteristics of nonstationary complex systems

Issued as final reportNational Science Foundation (U.S

A Practical Continually Online Trained Artificial Neural Network Controller for a Turbogenerator

This paper reports on the simulation and practical studies carried out on a single turbogenerator connected to an infinite bus through a short transmission line, with a continually online trained (COT) artificial neural network (ANN) controller to identify the turbogenerator, and another COT ANN to control the turbogenerator. This identifier/controller augments/replaces the automatic voltage regulator and the turbine governor. Results are presented to show that this COT ANN identifier/controller has the potential to allow turbogenerators to operate more closely to their steady-state stability limits and nevertheless “ride through” severe transient disturbances such as three phase faults. This allows greater usage of existing power plant

Decentralized Online Neuro-Identification of Turbogenerators in a Multi-Machine Power System

This paper proposes a new and a novel technique based on Artificial Neural Networks (ANNs) for nonlinear model of turbogenerators in a multi-machine power system. Only local measurements are required by each ANN in this new method, and hence it is called decentralized neuro-identificiation. Each turbogenerator in the power system is quipped with an ANN which is able to identify (or model) its particular turbogenerator from moment to moment This information can then be used by a second ANN at each generator to enable effective control of the nonlinear non-stationary process under all operating conditions. Simulation results are presented in this paper to show the potential of this new technique for designing fkture nonlinear ANN controllers