Designing power system stabilizer for multimachine power system using neuro-fuzzy algorithm

This paper describes a design procedure for a fuzzy logic based power system stabilizer (FLPSS) and adaptive neuro-fuzzy inference system (ANFIS) and investigates their robustness for a multi-machine power system. Speed deviation of a machine and its derivative are chosen as the input signals to the FLPSS. A four-machine and a two-area power system is used as the case study. Computer simulations for the test system subjected to transient disturbances i.e. a three phase fault, were carried out and the results showed that the proposed controller is able to prove its effectiveness and improve the system damping when compared to a conventional lead-lag based power system stabilizer controller

Design of Power System Stabilizer

A power system stabilizer (PSS) installed in the excitation system of the synchronous generator improves the small-signal power system stability by damping out low frequency oscillations in the power system. It does that by providing supplementary perturbation signals in a feedback path to the alternator excitation system. In our project we review different conventional PSS design (CPSS) techniques along with modern adaptive neuro-fuzzy design techniques. We adapt a linearized single-machine infinite bus model for design and simulation of the CPSS and the voltage regulator (AVR). We use 3 different input signals in the feedback (PSS) path namely, speed variation(w), Electrical Power (Pe), and integral of accelerating power (Pe*w), and review the results in each case. For simulations, we use three different linear design techniques, namely, root-locus design, frequency-response design, and pole placement design; and the preferred non-linear design technique is the adaptive neuro-fuzzy based controller design. The MATLAB package with Control System Toolbox and SIMULINK is used for the design and simulations

Improved Step Response of Power System Stabilizer using Fuzzy Logic Controller

As every power system is constantly being subjected to disturbances, we should see that these disturbances do not make the system unstable. Therefor additional signals derived from speed deviation, excitation deviation and accelerating power are injected into voltage regulators. The device to provide these signals is referred as power system stabilizer. The use of power system stabilizers has become very common in operation of large electric power systems. The conventional PSS which uses lead-lag compensation, where gain settings designed for specific operating conditions, is giving poor performance under different loading conditions. Therefore, it is very difficult to design a stabilizer that could present good performance in all operating points of electric power systems. In an attempt to cover a wide range of operating conditions, Fuzzy logic control has been suggested as a possible solution to overcome this problem. In this paper, a systematic approach to fuzzy logic control design is proposed. The study of fuzzy logic power system stabilizer for stability enhancement of a single machine infinite bus system is presented. In order to accomplish the stability enhancement, speed deviation and acceleration of the rotor synchronous generator are taken as the inputs to the fuzzy logic controller. These variables take significant effects on damping the generator shaft mechanical oscillations. The stabilizing signals were computed using the fuzzy membership function depending on these variables. The performance of the system with fuzzy logic based power system stabilizer is compared with the system having conventional power system stabilizer and system without power system stabilize

Dynamic Stability Improvement of Multimachine Power Systems using ANFIS-based Power System Stabilizer

Modern power system are very vurnerable to against load fluctuation during their operation. Load fluctuation identified as small disturbance is important to test dynamic (small signal) stability. This research is focused on improvement of multimachine dynamic stability by using ANFIS-based power system stabilizer (proposed PSS). ANFIS method is proposed because the ANFIS computation is more efective than Mamdani fuzzy computation. Simulation results show that the proposed PSS is able to maintain the dynamic stability by decreasing peak overshoot (Po) to the value -3,37´10-5 pu/pu and accelerating settling time (St) to the time 4.01 s for rotor speed deviation Machine-2. Also, the Po is decreased to the value -3,37´10-5 pu/pu and the St is accelerated to the time 3.98 s for rotor speed deviation Machine-3

Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

This paper presents a powerful supervisory power system stabilizer (PSS) using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS). The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC)-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC) driven by a fixed fuzzy set (FFS) which has 49 rules. Both fuzzy logic controller (FLC) algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study

Simulation of Synchronous Machine in Stability Study for Power System: Garri Station as a Case Study

This paper aimed to investigate the effectiveness of an adaptive artificial intelligence on enhancing the stability of gas turbine-Garri Power Station, which is designed to generate about 38MW. Fuzzy logic method controller was proposed based on stabilizer to enhance the power system stability. Linguistic rules and fuzzy inference mechanism are utilized to tune the controller parameters in different operating states. The proposed controller was applied to a single machine infinite bus system, represented by Gas Turbine-Garri Power Station (GPS). Simulation studies have been carried out using TILLSHELL software. The simulation results demonstrated that the proposed control scheme performs well and strongly control the power system under different loading conditions, disturbances and system parameter variations. The proposed controller is robust and more suitable for damping of low frequency oscillation and more effective in improving dynamic stability and voltage profile than the conventional systems

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

Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

This paper presents a powerful supervisory power system stabilizer (PSS) using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS). The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC)-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC) driven by a fixed fuzzy set (FFS) which has 49 rules. Both fuzzy logic controller (FLC) algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study