Nonlinear Modified PI Control of Multi-Module GCSCs in a Large Power System

This paper presents the design of a new control strategy for gate-controlled series compensators (GCSCs). GCSCs are new FACTS devices which can provide active power flow control on a transmission line. Proper placement of GCSCs in proximity to generators can also provide damping to system oscillations. This paper has investigated the effectiveness of multiple multi-module gate controlled series compensators (MMGCSCs) for large power systems. MMGCSCs can be less expensive devices with wide range of control of capacitive reactance in series with transmission lines. A nonlinear modified PI (NMPI) control is developed to provide power flow control and enhanced transient stability margin of the multi-machine power system. The NMPI is designed using a multi-layer neural network to approximate the blocking angle from the effective capacitive compensation provided by PI controller. A neural network with few neurons trained offline is used as an approximator /estimator for each MMGCSCs. This method has been shown effective for small and large disturbances on the IEEE 39 bus power system

Real-Time Implementation of an Optimal Transient Neurocontroller for a GCSC

This paper presents the design of an optimal Auxiliary Transient Neurocontroller (ATNC) for the Gate Controlled Series Capacitor (GCSC) in a multi-machine power system. GCSC is a recent advancement in the family of series FACTS devices. While GCSC regulates power flow through a transmission line, an auxiliary control signal can provide damping to the power oscillations during disturbances. The ATNC has been designed using heuristic dynamic programming to develop an optimal neurocontroller with fixed weights. The ATNC is implemented in real-time using the TMS3206701 DSP and the Real-Time Digital Simulator (RTDS). Results are presented to show the effectiveness of the ATNC in damping transient oscillations during disturbances

A Neural Network Based Optimal Wide Area Control Scheme for a Power System

With deregulation of the power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area control systems (WACSs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. This paper presents the design and the DSP implementation of a nonlinear optimal wide area controller based on adaptive critic designs and neural networks for a power system on the real-time digital simulator (RTDS©). The performance of the WACS as a power system stability agent is studied using the Kundur\u27\u27s two area power system example. The WACS provides better damping of power system oscillations under small and large disturbances even with the inclusion of local power system stabilizers

A Computational Approach to Optimal Damping Controller Design for a GCSC

This paper presents a computational approach for an offline measurement-based design of an optimal damping controller using adaptive critics for a new type of flexible ac transmission system device-the gate-controlled series capacitor (GCSC). Remote measurements are provided to the controller to damp out system modes. The optimal controller is developed based on the heuristic dynamic programming (HDP) approach. Three multilayer-perceptron neural networks are used in the design-the identifier/model network to identify the dynamics of the power system, the critic network to evaluate the performance of the damping controller, and the controller network to provide optimal damping. This measurement-based technique provides an alternative to the classical linear model-based optimal control design. The eigenvalue analysis of the closed-loop system is performed with time-domain responses using the Prony method. An analysis of the simulation results shows potential of the HDP-based optimal damping controller on a GCSC for enhancing the stability of the power system

DSP-Based PSO Implementation for Online Optimization of Power System Stabilizers

Real-time implementations of controllers require optimization algorithms which can be performed quickly. In this paper, a digital signal processor (DSP) implementation of particle swarm optimization (PSO) is presented. PSO is used to optimize the parameters of two stabilizers used in a power system. The controllers and PSO are both implemented on a single DSP in a hardware-in-loop configuration. Results showing the performance and feasibility for real-time implementations of PSO are presented

MISO Damping Controller Design for a TCSC Using Particle Swarm

This paper presents a new approach for designing multi-input-single-output (MISO) damping controller for a TCSC in a multi-machine power system. The damping controller design uses particle swarm optimization (PSO) to determine the coefficients of single or multi-stage lead-lag compensators. The classical technique works well in the design of lead-lag compensators for SISO controllers. But, there is no proper step-by-step procedure to achieve the desired performance characteristics for a MISO controller. Hence, in this paper, a computational optimization tool has been used to determine the optimal gains and time constants of a linear MISO damping controller. The damping controller is implemented for a TCSC on a multi-machine multi-modal power system and has shown considerable improvement in minimizing system oscillations

Comparison of Adaptive Critic-Based and Classical Wide-Area Controllers for Power Systems

An adaptive critic design (ACD)-based damping controller is developed for a thyristor-controlled series capacitor (TCSC) installed in a power system with multiple poorly damped interarea modes. The performance of this ACD computational intelligence-based method is compared with two classical techniques, which are observer-based state-feedback (SF) control and linear matrix inequality $hbox{LMI-H}^{infty}$ robust control. Remote measurements are used as feedback signals to the wide-area damping controller for modulating the compensation of the TCSC. The classical methods use a linearized model of the system whereas the ACD method is purely measurement-based, leading to a nonlinear controller with fixed parameters. A comparative analysis of the controllers\u27 performances is carried out under different disturbance scenarios. The ACD-based design has shown promising performance with very little knowledge of the system compared to classical model-based controllers. This paper also discusses the advantages and disadvantages of ACDs, SF, and $hbox{LMI-H}^{infty}$

Optimal Dynamic Neurocontrol of a Gate-Controlled Series Capacitor in a Multi-Machine Power System

This paper presents the design of an optimal dynamic neurocontroller for a new type of FACTS device - the gate controlled series capacitor (GCSC) incorporated in a multi-machine power system. The optimal neurocontroller is developed based on the heuristic dynamic programming (HDP) approach. In addition, a dynamic identifier/model and controller structure using the recurrent neural network trained with backpropagation through time (BPTT) is employed. Simulation results are presented to show the effectiveness of the dynamic neurocontroller and its performance is compared with that of the conventional PI controller under small and large disturbances

Wide-area Signal-based Optimal Neurocontroller for a UPFC

The design and implementation of an optimal neurocontroller for a flexible ac transmission device—the unified power-flow controller (UPFC)—is presented in this paper. Wide area signals in a power system are used to provide auxiliary control to a UPFC in order to achieve enhanced damping of system oscillations. The neurocontroller provides auxiliary signals to the real and reactive power references of a UPFC series inverter. The design of the optimal neurocontroller is based on an adaptive critic design approach—the heuristic dynamic programming. A system identifier, referred to as the wide-area monitor), and a critic network (performance evaluator) are designed for optimizing the neurocontroller. Real-time implementation of the optimal UPFC neurocontroller for a multimachine power system is carried out successfully on a digital signal processor. The power system is simulated on a real-time digital simulator. The performance of this neurocontroller is compared with a conventional linear damping controller. Results show enhanced damping of both inter-area and intra-area modes in the power system under different operating conditions and disturbances with the neurocontroller. The improvement in the damping is also quantified using the Prony method