Power System Stability Enhancement by Single Network Adaptive Critic Stabilizers

This paper proposes a Single Network Adaptive Critic (SNAC) based Power System Stabilizer (PSS) for enhancing the small-signal stability of power systems over a wide range of operating conditions. SNAC uses only a single critic neural network instead of the action-critic dual network architecture of typical adaptive critic designs. SNAC eliminates the iterative training loops between the action and critic networks and greatly simplifies the training procedure. The performance of the proposed PSS has been tested on a Single Machine Infinite Bus test system for various system and loading conditions. The proposed stabilizer, which is relatively easier to synthesize, consistently outperformed stabilizers based on conventional lead-lag and linear quadratic regulator designs.

Power System Stabilizers Design for Interconnected Power Systems

This paper proposes a method of designing fixed parameter decentralized power system stabilizers (PSS) for interconnected multi-machine power systems. Conventional design technique using a single machine infinite bus approximation involves the frequency response estimation called the GEP(s) between the AVR input and the resultant electrical torque. This requires the knowledge of equivalent external reactance and infinite bus voltage or their estimated values at each machine. Other design techniques using P-Vr characteristics or residues are based on complete system information. In the proposed method, information available at the high voltage bus of the step-up transformer is used to set up a modified Heffron-Phillip's model. With this model it is possible to decide the structure of the PSS compensator and tune its parameters at each machine in the multi-machine environment, using only those signals that are available at the generating station. The efficacy of the proposed design technique has been evaluated on three of the most widely used test systems. The simulation results have shown that the performance of the proposed stabilizer is comparable to that which could be obtained by conventional design but without the need for the estimation and computation of external system parameters.

Analysis of Torsional Interactions with Power System Stabilizer

Torsional interactions can occur due to the speed input Power System Stabilizer (PSS) that are primarily used to damp low frequency oscillations. The solution to this problem can be either in the form of providing a torsional filter or developing an alternate signal for the PSS. This paper deals with the formulation of a linearized state space model of the system and study of the interactions using eigenvalue analysis. The effects of the parameters of PSS and control signals on the damping of torsional modes are investigated.

Design and developement of a pulsed power system for a Vircator based HPM source

Pulse Forming Line (PFL) based high voltage pulsed power systems are well suited for low impedance High Power Microwave (HPM) sources such as a virtual cathode oscillator (VIRCATOR) operating in nanosecond regimes. The system under development consists of a primary voltage source that charges the capacitor bank of a Marx pulser over a long time duration. The Marx pulser output is then conditioned by a PFL to match the requirement of the HPM diode load. This article describes the design and construction of an oil insulated pulse forming line for a REB (Relativistic Electron Beam) diode used in a VIRCATOR for the generation of high power microwaves. Design of a 250 kV/10 kA/60 ns PFL, including the PSPICE simulation for various load conditions are described.

Improvement of power-system transient stability by phase-shift insertion

Computational studies of the transient stability of a synchronous machine connected to an infinite busbar by a double-circuit transmission line are used to illustrate the effect of relative phase-shift insertion between the machine and its associated power system. This method of obtaining a change in the effective rotor-excitation angle, and thereby the power transfer, is described, together with an outline of possible methods of implementation by a phase-shifting transformer in a power system.

Fuzzy logic based TCSC supplementary controller for damping of inter area oscillations in power system

This paper describes an application of a FACTS supplementary controller for damping of inter area oscillations in power systems. A fuzzy logic controller is designed to regulate a thyristor controlled series capacitor (TCSC) in a multimachine environment to produce additional damping in the system. Simultaneous application of the excitation controller and proposed controller is also investigated. Simulation studies have been done with different types of disturbances and the results are shown to be consistent with the expected performance of the supplementary controller.

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static VAr compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static Var compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system.

A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.