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

Adaptive Fuzzy Gain of Power System Stabilizer to Improve the Global Stability

The lead-lag power system stabilizer has several parameters to be optimized.In fact, the number of these latter increases with the number of generators constituting the multi-machine system.In this work, we propose anew approach of an adaptive and robust PSS; it achieves encouraging results by adjusting the gain using fuzzy logic and in the same time we use the same PSSs for each machine. In the first place, we could check that the gain is among the most critical parameters of the lead lag PSS. The parameters are globally optimized by the genetic algorithm, after that an expertise on the speed and the gain variations allow the value prediction according to the velocity deviation. To validate our results, a robustness test was made on a multimachine system IEEE (3 machines 9 bus), for different loads and the results showed good performance and robustness of the presented PSS

Multi-stage Fuzzy Power System Stabilizer based on Modified Shuffled Frog Leaping Algorithm

This paper presents a new strategy based on Multi-stage Fuzzy (MSF) PID controller for damping Power System Stabilizer (PSS) in multi-machine environment using Modified Shuffled Frog Leaping (MSFL) algorithm. The proposed technique is a new meta-heuristic algorithm which is inspired by mating procedure of the honey bee. Actually, the mentioned algorithm is used recently in power systems which demonstrate the good reflex of this algorithm. Also, finding the parameters of PID controller in power system has direct effect for damping oscillation. Hence, to reduce the design effort and find a better fuzzy system control, the parameters of proposed controller is obtained by MSFL that leads to design controller with simple structure that is easy to implement. The effectiveness of the proposed technique is applied to Single machine connected to Infinite Bus (SMIB) and IEEE 3-9 bus power system. The proposed technique is compared with other techniques through ITAE and FD

An Adaptive Mamdani Fuzzy Logic Based Controller for a Static Compensator in a Multimachine Power System

An adaptive Mamdani based fuzzy logic controller has been designed for controlling a static compensator (STATCOM) in a multimachine power system. Such a controller does not need any prior knowledge of the plant to be controlled and can efficiently control a STATCOM during different disturbances in the network. A model free approach using the controller output error is applied for training purposes that adaptively changes the controller output parameters based on a gradient descent method. Moreover, shrinking span membership functions are used for a more stable and accurate control performance. Simulation results show that the proposed controller outperforms the conventional PI controller during dynamic and transient disturbances

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