Coordination of Adaptive Neuro Fuzzy Inference System (ANFIS) and Type-2 Fuzzy Logic System-Power System Stabilizer (T2FLS-PSS) to Improve a Large-scale Power System Stability

Intelligent control included ANFIS and type-2 fuzzy (T2FLS) controllers grown-up rapidly and these controllers are applied successfully in power system control. Meanwhile, small signal stability problem appear in a large-scale power system (LSPS) due to load fluctuation. If this problem persists, and can not be solved, it will develop blackout on the LSPS. How to improve the LSPS stability due to load fluctuation is done in this research by coordinating of PSS based on ANFIS and T2FLS. The ANFIS parameters are obtained automatically by training process. Meanwhile, the T2FLS parameters are determined based on the knowledge that obtained from the ANFIS parameters. Input membership function (MF) of the ANFIS is 5 Gaussian MFs. On the other hand, input MF of the T2FLS is 3 Gaussian MFs. Results show that the T2FLS-PSS is able to maintain the stability by decreasing peak overshoot for rotor speed and angle. The T2FLS-PSS makes the settling time is shorter for rotor speed and angle on local mode oscillation as well as on inter-area oscillation than conventional/ ANFIS-PSS. Also, the T2FLS-PSS gives better performance than the other PSS when tested on single disturbance and multiple disturbances

A Novel Improved Sea-Horse Optimizer for Tuning Parameter Power System Stabilizer

Power system stabilizer (PSS) is applied to dampen system oscillations so that the frequency does not deviate beyond tolerance. PSS parameter tuning is increasingly difficult when dealing with complex and nonlinear systems. This paper presents a novel hybrid algorithm developed from incorporating chaotic maps into the sea-horse optimizer. The algorithm developed is called the chaotic sea-horse optimizer (CSHO). The proposed method is adopted from the metaheuristic method, namely the sea-horse optimizer (SHO). The SHO is a method that duplicates the life of a sea-horse in the ocean when it moves, looks for prey and breeds.  In This paper, The CSHO method is used to tune the power system stabilizer parameters on a single machine system. The proposed method validates the benchmark function and performance on a single machine system against transient response. Several metaheuristic methods are used as a comparison to determine the effectiveness and efficiency of the proposed method. From the research, it was found that the application of the logistics Tent map from the chaotic map showed optimal performance. In addition, the application of the PSS shows effective and efficient performance in reducing overshoot in transient conditions

Robust Design of FACTS Wide-Area Damping Controller Considering Signal Delay for Stability Enhancement of Power System

制度:新 ; 報告番号:甲3426号 ; 学位の種類:博士(工学) ; 授与年月日:2011/9/15 ; 早大学位記番号:新575

DESIGN OF REAL-TIME FUZZY LOGIC PSS BASED ON PMUs FOR DAMPING LOW FREQUENCY OSCILLATIONS

Poorly damped low frequency oscillations is one of the main problems threatening safe and stable operation of the interconnected power systems and reducing the capability of transmission the power. The generator's excitation system has been supplemented with the Power System Stabilizer (PSS) in order to improve the damping of these low oscillations. In the latest smart power grids, the Phasor Measurement Units (PMUs) become a fundamental element in the monitoring, protection and control applications as PMU signals are more accurate than the conventional measurement units and real time GPS stamped. In this study, Fuzzy Power System Stabilizer (FPSS) has been designed and its performance in damping inter-are oscillations compared with the conventional PSS (CPSS) based on the simulation with MATLAB/Simulink model. The results of the simulation with the Simulink model proved that the performance of the designed FPSS in damping inter-area oscillation is better than the CPSS. One of the main features of fuzzy controller is that it doesn't require mathematical modeling as it is designed based on the time-domain and the operator experience while, in contrast, the conventional PSS requires to be designed in the frequency domain. Real Time Digital Simulator (RTDS) has been used to develop the real-time models of the test systems. The time-domain simulations with the RTDS model when the system subjected to the large disturbance (three-phase to ground fault) have been performed to show that the designed FPSS improved the damping of the oscillations effectively. The simulation results have been verified by modal analysis

A Novel Approach to PID Controller Design for Improvement of Transient Stability and Voltage Regulation of Nonlinear Power System

In this paper, a novel design method for determining the optimal PID controller parameters for non-linear power system using the particle swarm optimization (PSO) algorithm is presented. The direct feedback linearization (DFL) technique is used to linearize the nonlinear system for computing the PID (DFL-PID) controller parameters. By taking an example of single machine infinite bus (SMIB) power system it has been shown that PSO based PID controller stabilizes the system and restores the pre-fault system performance after fault is cleared and line is restored. The performance of this controlled system is compared with the performance of DFL-state feedback controlled power system. It has been shown that the performance of DFL-PID controlled system is superior compared to DFL-state feedback controlled system. For simulation MATLAB 7 software is used.

A Novel Hybrid Prairie Dog Optimization Algorithm - Marine Predator Algorithm for Tuning Parameters Power System Stabilizer

The article presents the parameter tuning of the Power System Stabilizer (PSS) using the hybrid method. The hybrid methods proposed in this article are Praire Dog Optimization (PDO) and Marine Predator Algorithm (MPA). The proposed method can be called PDOMPA. In the PDOMPA method, the marine predator algorithm (MPA) is able to search around optimal individuals when updating population positions. MPA is used to make the exploration and exploitation stages of PDO more valid and accurate. PDO is an algorithm inspired by the life of prairie dogs. Prairie dogs are adapted to colonizing in burrows underground. Prairie dogs have daily habits of eating, observing for predators, establishing fresh burrows, or preserving existing ones. Meanwhile, MPA is a duplication of marine predator life which is modeled mathematically. In order to validate the performance of the PDOMPA method, this article presents a comparative simulation of the objective function and the transient response of PSS. This research uses validation by comparing with conventional methods, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Marine Predator Algorithm (MPA), and Praire Dog Optimization (PDO). Based on the simulation results, PDOMPA presents fast convergence in some cases and shows optimal results compared to competitive algorithms. From the simulation results using load variations, it was found that the proposed method has the ability to reduce the average undershoot and overshoot of speed by 42.2% and 85.37% compared to the PSS-Lead Lag method. Meanwhile the average settling time value of speed is 50.7%

Optimal Power Flow using Fuzzy-Firefly Algorithm

Development of Metaheuristic Algorithm in engineering problems grows really fast. This algorithm is commonly used in optimization problems. One of the metaheuristic algorithms is called Firefly Algorithm (FA). Firefly Algorithm is a nature-inspired algorithm that is derived from the characteristic of fireflies. Firefly Algorithm can be used to solve optimal power flow (OPF) problem in power system. To get the best performance, firefly algorithm can be combined with fuzzy logic. This research presents the application of hybrid fuzzy logic and firefly algorithm to solve optimal power flow. The simulation is done using the MATLAB environment. The simulations show that by using the fuzzy-firefly algorithm, the power losses, as well as the total cost, can be reduced significantly

Small-disturbance Angle Stability Enhancement using Intelligent Redox Flow Batteries

Small-disturbance angle stability or low-frequency oscillation is one of the important stability in the power system. Although damper windings and power system stabilizer (PSS) have been proved to stabilize and improve small-disturbance angle stability. However, due to increasing demand in the recent years, adding redox flow batteries (RFB) as additional devices is crucial. This paper investigates, the utilization additional devices called RFB to enhance the small-disturbance angle stability in the power system. Furthermore, ant colony optimization (ACO) method is used to tune RFB parameter. To analyze the stability improvement on the power system, single machine infinite bus is used as a test system. Eigenvalue and time domain simulation is used to examine the behavior of the investigated system. From the simulation, it is found that by installing RFB in the system, the small-disturbance angle stability of power system is improved and ACO can be a solution of tune RFB parameter

Dynamic Stability Enhancement Through the Application of Stabilizers of Electromechanical Oscillations

Power system dynamic stability is one of key issues system engineers face. Oscillations that regularly occur in the system, limit the transmission capability of the network. The need to study the stability of power systems has been increasingly growing along with the development of power systems and their grouping into large interconnections. The focus of this paper is determining the dynamic stability of a synchronous generator, and thus the power system, by applying the general theory of stability of dynamic systems. Furthermore, the procedure for the initial adjustment of the parameters of a conventional (IEEE3 type PSS1A) stabilizer of electromechanical oscillations is briefly described based on the frequency response analysis of a linear generator model also known as the Heffron-Phillips generator model