Abstract
In this research, an effective application and performance assessment of the Neuro-Fuzzy Controller (NFC) damping controller is designed to replace a single machine infinite bus (SMIB) power system stabilizer (PSS), and coordinated multi PSSs in large interconnected power systems are presented. The limitation of the conventional PSSs on SMIB and interconnected multi-machine test power systems are exposed and disclosed by the proposed NFC stabilizer. The NFC is a nonlinear robust controller which does not require a mathematical model of the test power system to be controlled, unlike the conventional PSSs’ damping controller. The Proposed NFC is designed to improve the stability of SMIB, an interconnected IEEE 3-machine, 9-bus power system, and an interconnected two-area 10-machine system of 39-bus New England IEEE test power system under multiple operating conditions. The proposed NFC damping controller performance is compared with the conventional PSS damping controller to confirm the capability of the proposed stabilizer and realize an improved system stability enhancement. The conventional PSSs’ design problem is transformed into an optimization problem where an eigenvalue-based objective function is developed and applied to design the SMIB-PSS and the interconnected multi-machine PSSs. The time-domain phasor simulation was done in the SIMULINK domain, and the simulation results show that the transient responses of the system rise time, settling time, peak time, and peak magnitude were all impressively improved by an acceptable amount for all the test system with the proposed NFC stabilizer. Thus, the NFC was able to effectively control the LFOs and produce an enhanced performance compared to the conventional PSS damping controller. Similarly, the result validates the effectiveness of the proposed NFC damping controller for LFO control, which demonstrates more robustness and efficiency than the classical PSS damping controller. Therefore, the application and performance of the NFC has appeared as a promising method and can be considered as a remarkable method for the optimal design damping stabilizer for small and large power systems.
Highlights
During power system operation, several undesired phenomena, such as disturbance, may affect the system response, which can render the system vulnerable to instability [1,2]
A robust and nonlinear Artificial Intelligence (AI) controller called Neuro-Fuzzy Controller (NFC), which does not require a mathematical model of the single machine infinite bus (SMIB), and interconnected multi-machine test power systems to be controlled were designed to replace the power system stabilizer (PSS) damping controller, in order to improve the stability of the test systems and offset the low-frequency oscillations (LFOs) for a system operating under multiple operating conditions
The proposed NFC stabilizer is a nonlinear robust controller which does not require a mathematical model of the test power system to be controlled, unlike the conventional PSS damping controller
Summary
Several undesired phenomena, such as disturbance, may affect the system response, which can render the system vulnerable to instability [1,2]. A new method for a novel fuzzy neural-PI-controller-based static synchronous series compensator (SSC) and a fuzzy-PSS with a novel structure were simultaneously developed in [37] for damping the power system oscillation on a 4-machine, 2-area, and 3-machine, 3-area power system Another novel fuzzy rule matrix was designed in [38] for fuzzy logic system PSS to effectively damp the low-frequency oscillations of a SMIB and 2-area, 4-machine, 10-bus power system. A robust and nonlinear AI controller called Neuro-Fuzzy Controller (NFC), which does not require a mathematical model of the SMIB, and interconnected multi-machine test power systems to be controlled were designed to replace the power system stabilizer (PSS) damping controller, in order to improve the stability of the test systems and offset the low-frequency oscillations (LFOs) for a system operating under multiple operating conditions.
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