Power system stabilizer based on a self-learning adaptive network fuzzy inference system

Malik, O.P.; Hariri, A.

doi:10.1191/0142331202tm052oa

Cited by 2 publications

(4 citation statements)

References 17 publications

(11 reference statements)

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“…The constraints of the Equation 12 are the PSS and IPFC damping controller parameter limits. Table 2 shows the parameters of the KH algorithm which are used for solving the optimization problem of Equation 12.…”

Section: Single-machine Infinite Bus Power Systemmentioning

confidence: 99%

“…Note that in the real‐world applications, tuning of the multi‐band PSS is a very challenging task, and thus the CPSS has gained a major role in the existing power system oscillation damping controllers. The complexity of power system stability studies reveals the necessity for development of intelligent algorithms in order to accurately damp the LFOs . Farahani replaced AVR and PSS with an intelligent controller based on neural networks and artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

“…The complexity of power system stability studies reveals the necessity for development of intelligent algorithms in order to accurately damp the LFOs. 1,[11][12][13] Farahani 11 replaced AVR and PSS with an intelligent controller based on neural networks and artificial intelligence. Although this control method is robust and adaptive, its application in practice needs complex hardware which makes it unusable in the real-world usages.…”

Section: Introductionmentioning

confidence: 99%

“…Although this control method is robust and adaptive, its application in practice needs complex hardware which makes it unusable in the real‐world usages. Authors in Malik and Hariri proposed a new intelligent PSS based on merging characteristics of neural networks and fuzzy logic. In Shayeghi et al, it is shown that PSS cannot provide appropriate damping for all power system modes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Application of reinforcement learning for generating optimal control signal to the IPFC for damping of low-frequency oscillations

Younesi

Shayeghi

Moradzadeh

2017

Int Trans Electr Energ Syst

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Summary In this paper, an adaptive design of interline power flow controller (IPFC) using a reinforcement learning (RL) approach is utilized for damping of the low‐frequency oscillations (LFOs) in power system. This Q‐learning‐based adaptive damping controller is applied to a single‐machine and multi‐machine power system. The main advantages of the Q‐learning based controller are its robustness and adaptive behavior to change in the operation condition; also, it does not need any knowledge about the control system, making the control strategy suitable for the realistic power systems with high nonlinearities. In order to demonstrate the performance of the proposed RL‐based damping controller in both single‐machine and multi‐machine power system, nonlinear simulations are carried out using MATLAB/Simulink. Three cases of simulations are performed; the system equipped with (1) only optimal classical power system stabilizer (PSS), (2) coordinated designed of PSS and IPFC, and (3) RL‐based optimized IPFC. Krill herd optimization algorithm is used for coordination design of PSS and IPFC in both single machine and multi‐machine power systems. Suitable time‐domain performance indices in multiple operating conditions and different fault types are calculated for all 3 cases of simulations and are compared with one another. Simulation results demonstrate the effectiveness of the proposed control strategy in damping of the LFOs in the power systems.

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Section: Single-machine Infinite Bus Power Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%