An intelligent EGWO‐SCA‐CS algorithm for PSS parameter tuning under system uncertainties

Devarapalli, Ramesh; Bhattacharyya, B.; Sinha, Ν.Κ.

doi:10.1002/int.22263

Cited by 49 publications

(19 citation statements)

References 78 publications

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“…The common features of (13), and ( 14) can be achieved with the objective function framed as given in (15). where the selection of multiplication coefficient (α) is based on the magnitudes of individual portions J 1 and J 2 , as the magnitude of the square of the low damping ratios is comparatively less than the square of the real parts, the α is selected as 1000 [39]. The considered objective functions and their operating regions are represented in Fig.…”

Section: Objective Function and Constraintsmentioning

confidence: 99%

Power and energy system oscillation damping using multi-verse optimization

Devarapalli

Bhattacharyya

2021

SN Appl. Sci.

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Power system oscillations are the primary threat to the stability of a modern power system which is interconnected and operates near to their transient and steady-state stability limits. Power system stabilizer (PSS) is the traditional controller to damp such oscillations, and flexible AC transmission system (FACTS) devices are advised for the improved damping performance. This paper suggests a technique for controller parameters tuning of PSS and a shunt connected FACTS device to be operated in coordination. A static synchronous compensator (STATCOM) connected in a two-machine system is considered as a test power system for the system studies. A recent meta-heuristic algorithm, Multi-Verse optimizer (MVO) has been suggested and compared with the other state-of-the-art algorithms. Improvement in system damping has been achieved by minimizing the oscillating nature of the system states by framing the objective function as a function of damping ratio and location of poles of the system. The Phillips-Heffron model of the test system has been designed by considering the system dynamics. The coordinated system behavior under the perturbation in system parameters has been observed satisfactory with the tuned controller parameters obtained from the suggested algorithm.

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Section: Objective Function and Constraintsmentioning

confidence: 99%

Power and energy system oscillation damping using multi-verse optimization

Devarapalli

Bhattacharyya

2021

SN Appl. Sci.

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“…Authors have proposed various optimization algorithms to overcome the drawbacks. Therefore, the optimization techniques can be utilized to tune or/and optimize the gain parameter such as Genetic Algorithm (GA), 6 Particle Swarm Optimization (PSO), 7,8 Teaching‐Learning‐Based Optimization (TLBO), 9 Bacteria Foraging Algorithm (BFA), 10,11 and Gray Wolf Optimization (GWO) 12,13 . These algorithms have been promising technologies for handling optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it resulted in a complex structure and decreased its utilization appropriately. In References 12,13, the parameters are optimized by using GWO at various operating conditions. Unfortunately, the optimization procedure is needed for eigenvector, eigenvalue, participation factor, and sensitivity factor calculations at each iteration.…”

Section: Introductionmentioning

confidence: 99%

Parameter tuning of PSS and STATCOM controllers using genetic algorithm for improvement of small‐signal and transient stability of power systems with wind power

Bhukya

Mahajan

2021

Int Trans Electr Energ Syst

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Summary This work demonstrates the impact and robust coordination control among the Doubly Induction Generator (DFIG) and Synchronous Generators (SGs) with Power Oscillation Damping (POD), Power System Stabilizer (PSS),and STATic synchronous COMpensator (STATCOM) on the critical Low‐Frequency Oscillations (LFOs). These modes occurred due to system uncertainty, which leads to power flow interruption and experiences instability. It is mitigated by the proposed location and optimal coordinated optimized gain parameters of the controller. The locations are determined using the deterministic method and power flow sensitivity analysis (PSS and STATCOM). The objective function for the Genetic Algorithm (GA) is chosen as the real part and damping ratio of eigenvalue deviation of rotor speed. The small‐signal stability is assessed by using eigenvalue analysis via linearization of a nonlinear system. The transient stability is performed through time‐domain simulation by creating a three‐phase fault. Both analyses are examined with and without POD, PSS, and STATCOM and their combination by substituting SG with the same capacity of Wind Farm (WF). It is also indicated that a combination of POD, PSS, and STATCOM enhances system stability. It is also observed that the WF could not significantly involve in the modes of oscillation alike to the SG but provides the damping and beneficial toward the stability. GA and optimal location improve the system damping characteristics over a wide range of system uncertainty. The effectiveness and robustness of the proposed approaches are verified by considering a two‐area modified test system. Highlights This work demonstrates the impact and robust coordination control among the DFIG and SGs with POD, PSS and STATCOM on the critical LFOs caused by the intermittent nature of wind power. The locations are determined using the deterministic method and power flow sensitivity analysis (PSS and STATCOM). The objective function for the GA is chosen as the real part and damping ratio of eigenvalue and rotor speed deviation. The obtained results show enhancement in system stability.

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“…A static var compensator (SVC) is suggested by many researchers for the reactive power support in the transmission system, which further improves the voltage stability of the power system. 6,7 For the lesser magnitude of voltages, the reactive power support of SVC diminishes. This will lead to the inefficient controller performance in the abnormal conditions of the power system.…”

Section: Introductionmentioning

confidence: 99%

Controller parameter tuning of a single machine infinite bus system with static synchronous compensator using antlion optimization algorithm for the power system stability improvement

Devarapalli

Bhattacharyya

Saw³

2020

Adv Control Appl

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The improvement of power system stability by damping oscillations in the system states is presented in this paper. An antlion optimization (ALO) algorithm is adopted to tune the damping device controller parameters. The ALO algorithm performance is investigated on the benchmark functions, and the same has been compared with the traditional particle swarm optimization (PSO) technique. The statistical analysis on the fitness achieved with the considered algorithms has been demonstrated using best, worst, mean, and SD values on the benchmark functions over 30 individual runs. Here power system stabilizer (PSS) and static synchronous compensator are considered as oscillation damping devices in the test power network. The analysis has been carried out in a sample power system network, and the proposed technique is compared with the conventional PSS and PSO. The results obtained from the designed test system recommends the superior performance characteristics of ALO in applying to the practical power system.

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An intelligent EGWO‐SCA‐CS algorithm for PSS parameter tuning under system uncertainties

Cited by 49 publications

References 78 publications

Power and energy system oscillation damping using multi-verse optimization

Power and energy system oscillation damping using multi-verse optimization

Parameter tuning of PSS and STATCOM controllers using genetic algorithm for improvement of small‐signal and transient stability of power systems with wind power

Controller parameter tuning of a single machine infinite bus system with static synchronous compensator using antlion optimization algorithm for the power system stability improvement

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