Design of Power System Stabilizer Using Phase Based Objective Function and Heuristic Algorithm

Hassan, Mohamed A.; Abido, M. A.; Aliyu, Abdulrahman

doi:10.1109/icmsao.2019.8880343

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…Ref [2] used Firefly Algorithm to tune the parameters of PIDbased PSS controller for two cases of parametric bounds. Various other algorithms have been also used like Chaotic Particle Swarm Optimization (CPSO) [3], Kho-Kho Optimization [4], Search and Rescue Algorithm [5], Cuckoo Search Algorithm [6], Fuzzy Particle Swarm Optimization [7], Henry Gas Solubility Optimization [8], Farmland Fertility Algorithm [9], Differential Evolution Algorithm [10], [11], Ant Colony Optimization [12], Sine Cosine Algorithm [13], Particle Swarm Optimization [14], Archimedes Optimization Algorithm [15], Water Cycle Moth-Flame Optimization [16], Immune Genetic Algorithm [17], etc. Jaya algorithm have been used in solving a number of engineering optimization problems.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Optimized Proportional Integral Derivative Based Power System Stabilizer Using Jaya Algorithm for Angular Stability Enhancement

Aondoungwa,

Ibitayo

2024

JERR

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This study presents an optimized Proportional Integral Derivative Based Power System Stabilizer (PIDPSS) using Jaya Algorithm for angular stability enhancement. Jaya algorithm introduced by Rao, is an optimization technique with few control parameters which is used to minimize the objective function F(K). The modeling and simulation were done using Matlab /Simulink software version R2021b on IEEE 14-Bus system and Single Machine Infinite Bus (SMIB). A three-phase fault was introduced into the network at system runtime of 5s with a fault clearing time of 0.1s. The result of the simulation of the IEEE 14 Bus system showed a 74% and 24% reduction in overshoot time of speed deviation for generators 1 and 2, with settling times of 2.5s and 4s, respectively, in the presence of PIDPSS. The load angle experienced a 14% and 19% reduction in overshoot with settling times of 2s and 2.5s, respectively in the presence of PIDPSS for generators 1 and 2, respectively. The Electrical Power result showed 27% and 6% reduction in overshoot time as well as settling times of 2.5s and 4s, respectively, for generator 1 and 2 in the presence of PIDPSS. The result of the simulation of SMIB system also showed a 25% reduction in overshoot time in relation to deviation speed at a settling time of 4s in the presence of PIDPSS. The Load Angle showed- a 13% decrease in overshoot time at 2s settling time in the presence of PIDPSS. Also, the Electrical Power result highlighted a 15% drop-in overshoot time and settles within 2s. These results affirms that the PIDPSS introduced improved overall system stability.

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Section: Review Of Related Literaturementioning

confidence: 99%

Optimized Proportional Integral Derivative Based Power System Stabilizer Using Jaya Algorithm for Angular Stability Enhancement

Aondoungwa,

Ibitayo

2024

JERR

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“…This calls for a robust design of damping controllers that can effectively address the various oscillation modes to achieve the desired stability. However, damping controller design is an optimization-centered process that requires the exploitation of various optimization algorithms [32]. The damping of power system oscillations with damping controllers mostly depends on the tuning strategy adopted in obtaining the optimal parameters [33].…”

Section: Offshore Platformmentioning

confidence: 99%

“…The damping of power system oscillations with damping controllers mostly depends on the tuning strategy adopted in obtaining the optimal parameters [33]. Time integral performance criteria for calculating the integral error of the damping controller [32] and the eigenvalue-based stability analysis on the multi-machine test system are employed to evaluate the performance of damping controllers.…”

Section: Offshore Platformmentioning

confidence: 99%

“…The eigenvalues are moved to the left of the complex s-imaginary plane's axis to increase stability [37]. A time integral performance criterion for figuring out the integral error of damping controllers can also be used in stability analysis [32]. The time error objective functions are used to decrease the rotor speed deviation error for angular stability.…”

Section: Offshore Platformmentioning

confidence: 99%

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Solar, Wind and Their Hybridization Integration for Multi-Machine Power System Oscillation Controllers Optimization: A Review

et al. 2022

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Massive growth in global electrical energy demand has necessitated a genuine exploration and integration of solar and wind energy into the electrical power mix. This incorporation goes a long way in improving the cumulative generated power capacity of the power system. However, wind and solar photovoltaic (PV) are intermittent in nature, making the provisioning of a good maximum power tracking (MPPT) scheme necessary. Furthermore, the integration is characterized by synchronization challenges and introduces various modes of power system oscillations as it is converter-driven. This greatly affects the overall stability of the integrated power mix. Consequently, various technological models have been designed to address these challenges ranging from MPPT schemes, phase-lock loop (PLL), virtual synchronous generator (VSG), power system stabilizers (PSS), flexible AC transmission system (FACTS), coordinated control and artificial intelligence (AI). In this work, a multi-machine power system model is reviewed for integration stability studies. Various technical solutions associated with the integration are also reviewed. MPPT, PLL, VSG, PSS, FACTS, coordinated control, and various optimization technique schemes used for damping controller design are discussed.

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Optimal tuning of fractional‐order proportional, integral, derivative and tilt‐integral‐derivative based power system stabilizers using Runge Kutta optimizer

El‐Dabah

Kamel

Abido

et al. 2021

Engineering Reports

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Low-frequency power system oscillation is of great concern as it may lead to power system instability. Moreover, this action will lead to the abate capability of electric power transfer. By introducing a stabilizing signal into the excitation system, it was possible to improve the damping in the system. The power system stabilizer (PSS) provides this signal. This manuscript aims to find the optimal tuning of three different PSS controllers using a recent optimization algorithm called Runge Kutta optimizer (RUN). Based on the obtained results, the RUN shows superiority and fast convergence over competitive algorithms. In addition, the fractional-order proportional, integral, derivative (FOPID) and tilt-integral-derivative (TID) based PSS shows great damping capability over the lead-lag, PI, and FOPID-based PSS. Also, the TID-based PSS achieves better performance indices in terms of the lowest maximum overshoot and minimum settling time. Hence, it is recommended for PSS based controller.

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Design of Power System Stabilizer Using Phase Based Objective Function and Heuristic Algorithm

Cited by 5 publications

References 22 publications

Optimized Proportional Integral Derivative Based Power System Stabilizer Using Jaya Algorithm for Angular Stability Enhancement

Optimized Proportional Integral Derivative Based Power System Stabilizer Using Jaya Algorithm for Angular Stability Enhancement

Solar, Wind and Their Hybridization Integration for Multi-Machine Power System Oscillation Controllers Optimization: A Review

Optimal tuning of fractional‐order proportional, integral, derivative and tilt‐integral‐derivative based power system stabilizers using Runge Kutta optimizer

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