Artificial Intelligence-Based Power System Stabilizers for Frequency Stability Enhancement in Multi-Machine Power Systems

Low frequency oscillations (LFOs) occur in a system of interconnected generators connected by weak interconnection. A power system stabilizer (PSS) is commonly used to improve the capacity of the power system dampening. Under a variety of operating conditions, traditional PSSs fail to deliver superi...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.166095-166116
Main Authors: Sabo, Aliyu, Wahab, Noor Izzri Abdul, Othman, Mohammad Lutfi, Jaffar, Mai Zurwatul Ahlam Binti Mohd, Acikgoz, Hakan, Nafisi, Hamed, Shahinzadeh, Hossein
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Control stability
Control systems design
Controllers
Damping
Design optimization
Design parameters
Dynamic models
Efficiency
farmland fertility algorithm
Fertility
Fluctuations
Frequency stability
Fuzzy control
Fuzzy logic
Generators
interline power flow controller
Load flow
Lowfrequency oscillations
Model testing
neuro-fuzzy controller
Optimization
Oscillators
Power flow
Power system stability
power systemstabilizers
Quantitative analysis
Simulation
Test systems
Time domain analysis
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Summary:Low frequency oscillations (LFOs) occur in a system of interconnected generators connected by weak interconnection. A power system stabilizer (PSS) is commonly used to improve the capacity of the power system dampening. Under a variety of operating conditions, traditional PSSs fail to deliver superior damping. To address this issue, a Farmland Fertility Algorithm (FFA-PSSs controller) was used to solve an optimization problem for optimal design of PSSs system parameters, and its performance efficiency was compared to GA and PSO-based PSSs controllers. In addition to PSS, flexible current transmission (FACTS) devices are widely used. PSSs controllers and FACTS devices are frequently constructed in tandem to improve the dampening efficiency of the system. In this study, an Interline Power Flow Controller (IPFC) FACTS device will be added to the PSSs controller to improve the power system's oscillatory stability. PSSs optimal design and supplemental controller of power fluctuations for IPFC were conducted out on WSCC multi-machine test systems using a system linear model. Using time-domain simulations and quantitative analysis, the proposed IPFC model was compared to the FFA-PSSs controller in terms of performance and efficiency. The main disadvantage of this technique is the difficulty in designing a dynamic IPFC model in test systems, as well as the burden of IPFC coordinated PSSs optimization. In both PSSs design using FFA method and FFA-optimized PSS with IPFC cases, rise in the computational and simulation costs was found unavoidable. To compensate for these flaws and obtain the research contribution, this paper proposes a Neuro-Fuzzy Controller (NFC) developed as a damping controller that can take the place of the two controllers (research objectives three). The application of the NFC substitute the computational and simulation cost involved in designing multi-machine PSS and IPFC-FACTS systems simultaneously. With the availability of NFC in SIMULINK, a dynamic model of the WSCC three-machine system was developed under a variety of operating situations. Quantitative analysis results from the WSCC test system simulation show that when comparing the proposed NFC model to the IPFC model for the WSCC test system, the proposed NFC model was found to be 149 percent and 0 percent efficient in terms of the time to settle of rotor angle respond for G2 and G3, respectively, but 394 percent efficient when compared to the uncontrolled model. The decreased settling
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3133285