Implementation of Adaptive Critic-Based Neurocontrollers for Turbogenerators in a Multimachine Power System

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller\u27s training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in system operating conditions and configurations. This paper also shows that it is possible to design and implement optimal neurocontrollers for multiple turbogenerators in real time, without having to do continually online training of the neural networks, thus avoiding risks of instability

Indirect Adaptive Control for Synchronous Generator: Comparison of MLP/RBF Neural Networks Approach with Lyapunov Stability Analysis

This paper compares two indirect adaptive neurocontrollers, namely a multilayer perceptron neurocontroller (MLPNC) and a radial basis function neurocontroller (RBFNC) to control a synchronous generator. The different damping and transient performances of two neurocontrollers are compared with those of conventional linear controllers, and analyzed based on the Lyapunov direct method

Evolutionary neurocontrol: A novel method for low-thrust gravity-assist trajectory optimization

This article discusses evolutionary neurocontrol, a novel method for low-thrust gravity-assist trajectory optimization

Neurocontroller Alternatives for Fuzzy Ball-and-Beam Systems with Nonuniform Nonlinear Friction

The ball-and-beam problem is a benchmark for testing control algorithms. Zadeh proposed (1994) a twist to the problem, which, he suggested, would require a fuzzy logic controller. This experiment uses a beam, partially covered with a sticky substance, increasing the difficulty of predicting the ball\u27s motion. We complicated this problem even more by not using any information concerning the ball\u27s velocity. Although it is common to use the first differences of the ball\u27s consecutive positions as a measure of velocity and explicit input to the controller, we preferred to exploit recurrent neural networks, inputting only consecutive positions instead. We have used truncated backpropagation through time with the node-decoupled extended Kalman filter (NDEKF) algorithm to update the weights in the networks. Our best neurocontroller uses a form of approximate dynamic programming called an adaptive critic design. A hierarchy of such designs exists. Our system uses dual heuristic programming (DHP), an upper-level design. To our best knowledge, our results are the first use of DHP to control a physical system. It is also the first system we know of to respond to Zadeh\u27s challenge. We do not claim this neural network control algorithm is the best approach to this problem, nor do we claim it is better than a fuzzy controller. It is instead a contribution to the scientific dialogue about the boundary between the two overlapping disciplines

Two Separate Continually Online-Trained Neurocontrollers for a Unified Power Flow Controller

The crucial factor affecting the modern power systems today is load flow control. The Unified Power Flow Controller (UPFC) provides an effective means for controlling the power flow and improving the transient stability in a power network. The UPFC has fast complex dynamics and its conventional control is based on a linearized model of the power system. This paper presents the design of neurocontrollers to provide better damping during transient and dynamic control. Two separate neurocontrollers are used for controlling the UPFC, one neurocontroller for the shunt inverter and the other for the series inverter. Simulation studies carried out in the PSCAD/EMTDC environment is described and results show the successful control of the UPFC and the power system with two neurocontrollers. Performances of the neurocontrollers are compared with the conventional proportional plus integral controllers for system oscillation damping under different operating conditions for large disturbances

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

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

Dual Heuristic Programming Excitation Neurocontrol for Generators in a Multimachine Power System

The design of optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP based neurocontrol is much more effective than the conventional PID control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for nonlinear systems, such as power systems, without having to continually online train the neural networks, thus avoiding risks of instability