A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems

The purpose of this article is to present a novel strategy for the coordinated design of the Thyristor Controlled Series Compensator (TCSC) controller and the Power System Stabilizer (PSS). A time domain objective function that is based on an optimization problem has been defined. This objective function takes into account not only the influence that disturbances have on the mechanical power, but also, and this is more accurately the case, the impact that disturbances have on the reference voltage. When the objective function is minimized, potential disturbances are quickly mitigated, and the deviation of the speed of the generator's rotor is limited; as a result, the system's stability is ultimately improved. Particle Swarm Optimization (PSO) and the Shuffled Frog Leaping Algorithm are both components of a composite strategy that is utilized in the process of determining the optimal controller parameters. (SFLA). An independent controller design as well as a collaborative controller design utilizing PSS and TCSC are developed, which enables a direct evaluation of the functions performed by each. The presentation of the eigenvalue analysis and the findings of the nonlinear simulation can help to provide a better understanding of the efficacy of the outcomes. The findings indicate that the coordinated design is able to successfully damp low-frequency oscillations that are caused by a variety of disturbances, such as changes in the mechanical power input and the setting of the reference voltage, and significantly enhance system stability in power systems that are connected weekly

Decentralized Synergetic Control of Power Systems

The objective of this dissertation is to design decentralized controllers to enhance the transient stability of power systems. Due to the nonlinearities and complexities of the system, nonlinear control design techniques are required to improve its dynamic performance. In this dissertation a synergetic control technique is being proposed to design supplementary controller that is added to the exciter of the generation unit of the system. Although this method has been previously applied to a Single Infinite Machine Bus (SMIB) system with high degree of success, it has not been employed to systems with multi machine. Also, the method has good robust characteristic like that of the popular Sliding Mode Control (SMC) technique. But the latter technique introduces steady state chattering effect which can cause wear and tear in actuating system. This gives the proposed technique a major advantage over the SMC. In this work, the method is employed for systems with multi machine. Each of the machines is considered to be a subsystem and decentralized controller is designed for each subsystem. The interconnection term of each subsystem with the rest of the system is estimated by a polynomial function of the active power generated by the subsystem. Particle Swarm Optimization (PSO) technique is employed for optimum tuning of the controller\u27s parameters. To further enhance the performance of the system by widening its range of operation, Reinforcement Learning (RL) technique is used to vary the gains of the decentralized synergetic supplementary controller in real time. The approach is illustrated with several case studies including a SMIB system with or without a Static Var Compensator (SVC), a Two Area System (TAS) with or without an SVC, a three --machines-nine-bus system and a fifty machine system. Results show that the proposed control technique provides better damping than the conventional power system stabilizers and synergetic controllers with fixed gains

Application of swarm mean-variance mapping optimization on location and tuning damping controllers

This paper introduces the use of the Swarm Variant of the Mean-Variance Mapping Optimization (MVMO-S) to solving the multi-scenario problem of the optimal placement and coordinated tuning of power system damping controllers (POCDCs). The proposed solution is tested using the classical IEEE 39-bus test system, New England test system. This papers includes performance comparisons with other emerging metaheuristic optimization: comprehensive learning particle swarm optimization (CLPSO), genetic algorithm with multi-parent crossover (GA-MPC), differential evolution DE algorithm with adaptive crossover operator, linearized biogeography-based optimization with re-initialization (LBBO), and covariance matrix adaptation evolution strategy (CMA-ES). Numerical results illustrates the feasibility and effectiveness of the proposed approach

Advances and Trends in Mathematical Modelling, Control and Identification of Vibrating Systems

This book introduces novel results on mathematical modelling, parameter identification, and automatic control for a wide range of applications of mechanical, electric, and mechatronic systems, where undesirable oscillations or vibrations are manifested. The six chapters of the book written by experts from international scientific community cover a wide range of interesting research topics related to: algebraic identification of rotordynamic parameters in rotor-bearing system using finite element models; model predictive control for active automotive suspension systems by means of hydraulic actuators; model-free data-driven-based control for a Voltage Source Converter-based Static Synchronous Compensator to improve the dynamic power grid performance under transient scenarios; an exact elasto-dynamics theory for bending vibrations for a class of flexible structures; motion profile tracking control and vibrating disturbance suppression for quadrotor aerial vehicles using artificial neural networks and particle swarm optimization; and multiple adaptive controllers based on B-Spline artificial neural networks for regulation and attenuation of low frequency oscillations for large-scale power systems. The book is addressed for both academic and industrial researchers and practitioners, as well as for postgraduate and undergraduate engineering students and other experts in a wide variety of disciplines seeking to know more about the advances and trends in mathematical modelling, control and identification of engineering systems in which undesirable oscillations or vibrations could be presented during their operation

Design of a Wide Area Controller Using Eigenstructure Assignment in Power Systems

Small signal stability has become a major concern for power system operators around the world. This has resulted from constantly evolving changes in the power system ranging from increased number of interconnections to ever increasing demand of power. In highly stressed operating conditions, even a small disturbance such as a load change can make the system unstable resulting in small signal instability. The main reason for small signal instability in power systems is an inter-area mode/s becoming unstable. Inter-area modes involve a group of generators oscillating against each other. Traditionally, power system stabilizers installed on the synchrous machines were used to damp the inter-area modes. However, they may not be very suitable to perform the job since they use local I/O signals which do not have a good controllability/observability of the inter-area modes. Recent advancements in phasor measurement technology has resulted in fast acquisition of time-synchronized measurements throughout the system. Thus, instead of using local controllers, an idea of a wide area controller (WAC) was proposed by the power systems community that would use global signals. This dissertation demonstrates the design of a WAC using eigenstructure assignment technique. This technique provides the freedom to assign a few eigenvalues and corresponding left or right eigenvectors for Multi-Input-Multi-Output (MIMO) systems. This technique forms a good match for designing a WAC since a WAC usually uses multiple I/O signals and a power system only has a few inter-area modes that might lead to instability. The last chapter of this dissertation addresses an important aspect of controller design, i.e., robustness of the controller to uncertainties in operating point and time delay of feedback signals. The operating point of a power system is highly variable in nature and thus the designed WAC should be able to damp the inter-area modes under these variations. Also, a transmission delay is associated due to routing of remote signals. This time delay is known to deteriorate the performance of the controller. A single controller will be shown to achieve robustness against both these uncertainties

Application of differential evolution to power system stabilizer design

Includes synopsis.Includes bibliographical references.In recent years, many Evolutionary Algorithms (EAs) such as Genetic Algorithms (GAs) have been proposed to optimally tune the parameters of the PSS. GAs are population based search methods inspired by the mechanism of evolution and natural genetic. Despite the fact that GAs are robust and have given promising results in many applications, they still have some drawbacks. Some of these drawbacks are related to the problem of genetic drift in GA which restricts the diversity in the population. ... To cope with the above mentioned drawbacks, many variants of GAs have been proposed often tailored to a particular problem. Recently, several simpler and yet effective heuristic algorithms such as Population Based Incremental Learning (PBIL) and Differential Evolution (DE), etc., have received increasing attention