Series Capacitive Reactance Compensator Research Articles

Design of PSS and SCRC Controllers to Damp out Low-Frequency Oscillations in Power System

The paper describes a method to damp out low-frequency oscillations by design and tuning of both power system stabilizer (PSS) and controllable series capacitive reactance compensator (SCRC) controllers in power system. The PSS is generally used to mitigate the low-frequency oscillation by injecting supplementary signal to automatic voltage regulator (AVR) and the SCRC is used to enhance the transfer capacity for the transmission line in power system. Especially, the design of the external controller of the SCRC can provide effective damping for the low-frequency oscillation. In the paper, the effect of the design of both controllers on the system stability is analyzed. Also, the parameter tuning method is introduced and the performances of the proposed method are carried out by time-domain simulation with single-machine connected to infinite bus.

Nonlinear Parameter Optimization of FACTS Controller via Real-Time Digital Simulator

This paper describes a method for implementing a gradient-based nonlinear optimization technique for optimal tuning of nonlinear power system controllers such as a flexible ac transmission system (FACTS) via a combination of hardware and software in the real-time digital simulator (RTDS). The RTDS offers fundamental advantages over other commercial software tools because it is a fully digital power system simulator that operates in real time. Therefore, nonlinear optimal parameters can be effectively determined under similar environments to actual physical systems by this real-time implementation. In this paper, the Broyden-Fletcher-Goldfarb-Shanno optimization algorithm, which has a quadratic convergence property, is implemented in order to design the nonlinear controllers in power systems. Its effectiveness is demonstrated with two design examples intended to improve low-frequency oscillation damping in power systems. After illustrating an example to optimize the nonlinear parameters of the power system stabilizer, this paper introduces the optimal design of an external controller for the series capacitive reactance compensator, which is one of the FACTS devices, in real-time simulation.

Indirect Adaptive External Neuro-Control for a Series Capacitive Reactance Compensator Based on a Voltage Source PWM Converter in Damping Power Oscillations

A series capacitive reactance compensator (SCRC), using a voltage source converter to inject a controllable voltage in quadrature with the line current of a power network, is capable of rapidly providing a specified and controllable magnitude of capacitive reactance compensation independent of the line current. Moreover, with a suitably designed external controller, the SCRC can also be used to damp low-frequency power oscillations in a power network. Conventionally, linear control techniques are used to design the SCRC external controller around a specific operating point, where the nonlinear system equations are linearized. However, at other operating points its performance degrades. Nonlinear adaptive neuro-controllers offer an attractive approach to overcome this degradation problem. In this paper, an indirect adaptive external neuro-controller (INDAEC) using two radial basis function neural networks (RBFNNs) is proposed to improve the damping performance of an SCRC connected to a power network. This nonlinear INDAEC needs no mathematical model of the SCRC or the power network. It provides the SCRC with improved damping performance over a wide range of system operating conditions. This is shown by results on a single machine infinite bus power system, as well as a multimachine power system

Secondary Control for a Series Reactive Compensator Based on a Voltage-Source PWM Inverter

This work describes a secondary control scheme of a series reactive compensator for a power system based on a single voltage-source pulse-width-modulated (PWM) inverter. The controllable capacitive reactance can be used as a supplementary control variable for the secondary (external) controller (SC) of a series capacitive reactance compensator to improve the dynamic transient and damping performances of the power system. From the viewpoint of agent-based global dynamic optimization of a system, the selection and use of suitable input signals for the SC are investigated. Detailed simulation results show that the SC with local feedback loop (LFL) has a powerful control performance; however, it requires the controllable compensation for a reference change due to different operating conditions. On the other hand, the SC with global feedback loop (GFL) avoids the need of reference compensation; moreover, its dynamic control performance is improved when the dual inputs (frequency and active power signal) are used, compared to when only the frequency is used as an input signal.

Decentralized optimal neuro-controllers for generation and transmission devices in an electric power network

In this paper, the dual heuristic programming (DHP) optimization algorithm is applied for the design of two LOCAL nonlinear optimal neuro-controllers on a practical multi-machine power system. One neuro-controller is designed to replace the conventional linear controllers, which are the automatic voltage regulator (AVR) and speed-governor (GOV), for a synchronous generator. The other is a new external neuro-controller for the series capacitive reactance compensator (SCRC), flexible ac transmission systems (FACTS) device. The PSCAD/EMTDC ® simulation results show that interactions of two DHP neuro-controllers with different control objectives improve the system performance more effectively compared to when each one operates without the presence of the other one.

New External Neuro-Controller for Series Capacitive Reactance Compensator in a Power Network

The controllable capacitive reactance can be used as the input variable for the external controller of a series capacitive reactance compensator (SCRC) to improve the damping of low-frequency oscillations of the rotor angle and active power in a power system. Conventional linear PI controllers are tuned for best performance at one specific operating point of the nonlinear power system. At other operating point its performance degrades. Nonlinear optimal neuro-controllers are able to overcome this degradation. In this paper, the dual heuristic dynamic programming (DHP) optimization algorithm is applied to design an external nonlinear optimal neuro-controller for the SCRC. Simulation studies using PSCAD/EMTDC/spl reg/ software are presented.