Investigation of Single-Band and Multi-Band Power System Stabilizers Towards Transient Stability Improvement in Electrical Networks

Alsakati, Ahmad Adel; Vaithilingam, Chockalingam Aravind; Alnasseir, Jamal; Jagadeeshwaran, Arthanari

doi:10.1109/cencon51869.2021.9627246

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…This adaptive speed adjustment enables the male mayfly to optimize its movement strategy when it perceives an improvement in fitness. The process is mathematically represented in Equation (7).…”

Section: The Actions Of Male Mayfliesmentioning

confidence: 99%

“…Concurrently, the swing of one generator in relation to another in a different area is associated with the inter-area mode [6]. When the frequency falls between 0.01 and 0.05 Hz, all swinging generators are connected to the global mode [7]. This oscillation mode can have significant adverse effects, such as constraining the transmission capacity or disrupting loads.…”

Section: Introductionmentioning

confidence: 99%

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Stability Improvement of Sulselrabar System With Integrated Wind Power Plant Using Multi-Band PSS3C Based Mayfly Optimization Algorithm

Ruswandi Djalal,

Robandi,

Almas Prakasa

2024

IEEE Access

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The introduction of additional controllers is essential in modern electric power systems to enhance their stability, particularly during disturbances. One effective method is the implementation of power system stabilizers (PSS). However, precise coordination of PSS equipment is necessary to determine the optimal locations and parameters. This study focuses on the optimal analysis of Multi-Band PSS3C (MB-PSS3C) coordination in integrated Wind Power Plant (WPP) systems in South, Southeast, and West Sulawesi (Sulselrabar). An artificial intelligence approach, utilizing the Mayfly Optimization Algorithm (MOA), is suggested for optimizing both the location and parameters of the PSS. Comparative investigations were conducted to assess the efficacy of MB-PSS3C in comparison with SB-PSS1A and MB-PSS2B, based on previous research. The performance analysis employed the time domain simulation method, reviewing the speed deviation response, field voltage response, PSS output voltage response, and rotor angle response for each generator. Eigenvalue analysis was performed for each control scheme. Load changes were applied to generators 1 (BAKARU) and 11 (WPP SIDRAP) to evaluate the performance of the system. The application of the MOA-based MB-PSS3C results in an increased damping ratio, improved speed response, and a more optimal rotor angle. MB-PSS3C provides a larger additional damping signal to the generator exciter, as indicated by the increase in the field voltage on the generator.

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Section: The Actions Of Male Mayfliesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stability Improvement of Sulselrabar System With Integrated Wind Power Plant Using Multi-Band PSS3C Based Mayfly Optimization Algorithm

Ruswandi Djalal,

Robandi,

Almas Prakasa

2024

IEEE Access

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“…Evaluation of the installation results showed an increase in the damping ratio of the system. In [36], the MB-PSS design was investigated using a two-area four-engine system. The results show that the performance of the optimized MB-PSS is better than that of the conventional PSS, where the power-angle settling time is reduced.…”

Section: Introductionmentioning

confidence: 99%

Stability Enhancement of Sulselrabar Electricity System Using Mayfly Algorithm Based on Static Var Compensator and Multi-Band Power System Stabilizer PSS2B

Djalal,

Robandi,

Prakasa

2023

IEEE Access

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An additional controller in an electric power system is currently required to increase the system stability, especially when a disturbance occurs. The stability of the multimachine system can be increased by installing a Static Var Compensator (SVC) and Power System Stabilizer (PSS). However, SVC and PSS equipment require precise coordination to determine the optimal location and parameters. This study presents an optimal analysis of SVC coordination with single-band PSS1A and multi-band PSS2B (MB-PSS2B) in the South, Southeast and West Sulawesi (Sulselrabar) electrical systems. An artificial intelligence method based on the Mayfly Optimization Algorithm (MOA) is proposed to optimize the location and parameters of the SVC and PSS. A comparative investigation related to controller parameter optimization from a previous work was used to measure the effectiveness of the MOA based on the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). Performance analysis using the time-domain simulation method to review the speed deviation response, field voltage response, and rotor angle response for each generator, as well as eigenvalue analysis for each control scheme when there is a change in the load disturbance on generators 1 and 13. The results show an increase in bus voltage from critical to marginal conditions and a decrease in network losses after installing SVC on bus 31 of 40 MW capacity. The application of MB-PSS2B based on the MOA provided an increased damping ratio, optimal speed response, rotor angle, field voltage generator, and eigenvalue system after installing 14 PSS.

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“…Improved PSS can cope with different oscillation modes [7], [18]. This requirement led to the development of MB-PSS with multi-band transfer functions that offer higher levels of flexibility and reliability compared to Conventional PSS (CPSS) for the different oscillation modes [7], [18], [19]. MB-PSS4B is developed based on multiple frequency bands and consists of three bands that are correlated with different oscillation modes.…”

Section: Introductionmentioning

confidence: 99%

Transient Stability Enhancement of Grid Integrated Wind Energy Using Particle Swarm Optimization Based Multi-Band PSS4C

et al. 2022

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There is a growing need for stability enhancement in modern electrical networks integrated with wind energy, particularly due to different oscillation modes disturbances. Power system stabilizers (PSSs) are used to mitigate oscillations and improve the stability of the power system. This paper presents a comprehensive analysis of single-band PSS1A and multi-band PSS4C (MB-PSS4C) connected to the ST1A excitation system. An efficient approach in the selection of the parameters of MB-PSS4C using Particle Swarm Optimization (PSO) is proposed. A comparative investigation linked to the common Pattern Search and Simplex Search, from previous work, has been conducted to gauge the efficacy of PSO. The generator's transient stability with considerations of relative power angle (power angle differences), speed deviation, and active power of synchronous generators (SGs) are analyzed. Different wind penetration levels ranging from 36MW to 108 MW are integrated into the network and are investigated. Results demonstrate that PSO-MB-PSS4C connected to ST1A stabilizes the system effectively with reduced settling time while mitigating the peak power angle differences of SGs. Reduced settling time of 1.35 s is observed for wind penetration of 48 MW, with 43.31 ͦ peak power angle; while for high wind penetration of 108 MW, the settling time is around 8 s with peak power angle of 43.9 ͦ .

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Investigation of Single-Band and Multi-Band Power System Stabilizers Towards Transient Stability Improvement in Electrical Networks

Cited by 5 publications

References 21 publications

Stability Improvement of Sulselrabar System With Integrated Wind Power Plant Using Multi-Band PSS3C Based Mayfly Optimization Algorithm

Stability Improvement of Sulselrabar System With Integrated Wind Power Plant Using Multi-Band PSS3C Based Mayfly Optimization Algorithm

Stability Enhancement of Sulselrabar Electricity System Using Mayfly Algorithm Based on Static Var Compensator and Multi-Band Power System Stabilizer PSS2B

Transient Stability Enhancement of Grid Integrated Wind Energy Using Particle Swarm Optimization Based Multi-Band PSS4C

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