Detection of shorted-turns in the field winding ofturbine-generator rotors using novelty detectors-development and fieldtest

A method for detecting shorted windings in operational turbine-generators is described. The method is based on the traveling wave method described by El-Sharkawi, et. al. (1971). The method is extended in this paper to operational rotors by the application of a neural network feature extraction and novelty detection. The results of successful laboratory experiments are also reported     

Excitation control design of local machines in a large-scale power system by using dynamic equivalencing technique

A linear optimal control design for machine excitation is developed to improve the local system stability in a large-scale power system. Dynamic equivalents for the external system are used to provide the controller with information about the original external system. The paper investigates whether external system information helps the controller to stabilize the local system and whether the dynamic equivalents are a reliable source of that information. The validity and accuracy of the proposed method is demonstrated by simulated tests on two sample power systems.     

High performance drive of DC brushless motors using neural network

In this paper, a multi-layer neural network (NN) architecture is proposed for the identification and control of DC brushless motors operating in a high performance drives environment. The NN in the proposed structure performs two functions. The first is to identify the nonlinear system dynamics at all times. Hence, detailed and elaborate models for the DC brushless machines are not needed. Furthermore, unknown nonlinear dynamics that are difficult to model such as load disturbances, system noise and parameter variations can be recognized by the trained neural network. The second function of the NN is to control the motor voltage so that the speed and position are made to follow pre-selected tracks (trajectories) at all times. The control action emulated by the NN is based on the indirect model reference adaptive control. A hardware laboratory setup is utilized to test and evaluate the proposed neural network structure. The paper shows, based on the laboratory test results, that the proposed neural network structure performance was good: the tracking accuracy was very high and the system robustness was quite evident even in the presence of random and severe disturbances     

Neural networks' power

Neural networks (NNs) are effective systems for learning pattern discriminants from a body of examples. Artificial neural networks (ANNs) have been developed in a wide variety of configurations with some common underlying characteristics. All ANNs attempt to achieve good performance via massive interconnection of simple computational elements. Neural networks are characterized by the model of their neurons, the connections among them and the methods used to train them to do specific tasks. The author describes multi-layer neural networks and Kohonen neural networks. The author then discusses how they are used in electric load forecasting and power system security assessment     

Inversion of feedforward neural networks: algorithms andapplications

There are many methods for performing neural network inversion. Multi-element evolutionary inversion procedures are capable of finding numerous inversion points simultaneously. Constrained neural network inversion requires that the inversion solution belong to one or more specified constraint sets. In many cases, iterating between the neural network inversion solution and the constraint set can successfully solve constrained inversion problems. This paper surveys existing methodologies for neural network inversion, which is illustrated by its use as a tool in query-based learning, sonar performance analysis, power system security assessment, control, and generation of codebook vectors     

Laboratory implementation of a neural network trajectory controllerfor a DC motor

The laboratory implementation of a neural network controller for high performance DC drives is described. The objective is to control the rotor speed and/or position to follow an arbitrarily selected trajectory at all times. The control strategy is based on indirect model reference adaptive control (MRAC). The motor characteristics are explicitly identified through a multilayer perceptron type neural network. The output of the trained neural network is used to drive the motor in order to achieve a desired time trajectory of the controlled variable. The neural network controller is assembled in a commercially available PC-based real-time control system shell, using software subroutines. An H-bridge, DC/DC voltage converter is interfaced with the computer to generate the specified terminal voltage sequences for driving the motor. All software and hardware components are off the shelf. The versatility of the motor/controller arrangement is displayed through real-time plots of the controlled states     

Tracking control technique for induction motors

A novel application is presented of the tracking control technique to induction motor drive systems. By this technique, the position or the speed of the rotor can follow a preselected track (a time history of rotor position or velocity). An algorithm for the design of the tracking controller is developed. The induction motor model and the controller are modified to allow the inclusion of the nonlinear modes in the system without excessive computations. A simple and realistic criterion for selecting the proper reference tracks during starting, speed control and braking is proposed. The controller developed, is tested on a full-size nonlinear analog simulator. All test results show the effectiveness of the scheme in position-tracking applications such as robotics and manipulators     

Development of a technique for on-line detection of shorts in fieldwindings of turbine-generator rotors: circuit design and testing

A technique for online detection of incipient faults in the windings of turbine-generator rotors has been developed based on the twin signal sensing method. This paper describes the development of power electronic circuits for generation of the twin signals and detection of the reflected signals. The design and fabrication of a lab model to test this technique is summarized along with results of laboratory experiments. The issues involved in using the developed technique for practical applications are addressed and the limitations of the technique are summarized     

Power system security assessment using neural networks: featureselection using Fisher discrimination

One of the most important considerations in applying neural networks to power system security assessment is the proper selection of training features. Modern interconnected power systems often consist of thousands of pieces of equipment each of which may have an effect on the security of the system. Neural networks have shown great promise for their ability to quickly and accurately predict the system security when trained with data collected from a small subset of system variables. This paper investigates the use of Fisher's linear discriminant function, coupled with feature selection techniques as a means for selecting neural network training features for power system security assessment. A case study is performed on the IEEE 50-generator system to illustrate the effectiveness of the proposed techniques