Power and energy system oscillation damping using multi-verse optimization

Devarapalli, Ramesh; Bhattacharyya, B.

doi:10.1007/s42452-021-04349-2

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…The primary threat to the stability of a modern power system, which is interconnected and operates close to its transient and steady-state stability limits, are power system oscillations. In view of this, a recent meta-heuristic algorithm, MVO has been suggested in [30] for selecting optimal parameters setting of the coordinated system based on PSS and STATCOM to enhance the stability system under a variety of multi-machine power system loading conditions. In this work, optimization results for the designed control model system have been illustrated and compared to others meta-heuristic methods like GWO, WOA, GWO and PSO-TVAC, can be demonstrated the effectiveness of this applied MVO algorithm.…”

Section: Mvo Algorithmmentioning

confidence: 99%

“…These methods are easier to find the best solution to problems than traditional methods. This group can be classified into four categories [18], (i)Evolutionary algorithms like Genetic Algorithms (GA) [19], Evolution Strategy (ES) [20], Evolutionary Programming (EP) [21], Genetic Programming (GP) [22], (ii) Physics-based algorithms contain Ant Lion Optimization (ALO) technique [23], Biogeography Based Optimizer (BBO) [24], Curved Space Optimization (CuSO) [25], Flower Pollination Algorithm (FPA) [26], Galaxy-based Search Algorithm (GBSA) [27], Gravitational Search Algorithm (GSA) [28], Harmony Search Algorithm (HAS) [29], Multi-Verse Optimization (MVO) Algorithm [30], Simulated Annealing (SA) [31], Atom Search Optimization (ASO) Algorithm [32], etc. (iii) Swarm Based algorithms such as Particle Swarm Optimization (PSO) [33], Whale optimization algorithm (WOA) [34], Artificial Bee Colony (ABC) [35], Chemical Reaction Optimization (CRO) algorithm [36], Crow Search Algorithm (CSA) [37], Cat Swarm Optimization (CaSO) algorithm [38], Cuckoo search (CS) [39], Dragonfly Algorithm (DA) [40], Bats Algorithm (BA) [41], Firefly algorithm (FFA) [42], Grasshopper optimization algorithm (GOA) [43], Grey Wolf Optimizer (GWO) [44], Honey-Bee Mating Optimization (HBMO) [45], Moth-Flame Optimization (MFO) algorithm [46], Bacterial Swarm Optimization (BSO) [47], Immune Algorithm (IA) [48], Symbiotic Organism Search (SOS) Algorithm [49], etc.…”

Section: Introductionmentioning

confidence: 99%

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Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Ismail¹,

Guesmi²,

Alshammari³

et al. 2023

Preprint

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Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

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Section: Mvo Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Ismail¹,

Guesmi²,

Alshammari³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The effective damping of low-frequency oscillations caused by different operating conditions depends on the gain and time constants selection in (13). The optimal values by the proposed WGWO algorithm can be achieved by minimizing the oscillating angular velocity, (𝜔), and maximizing the damping ratio, (𝜁), of the different oscillating modes.…”

Section: Mathematical Model Of Test Power Systemmentioning

confidence: 99%

“…Also, as the network size of an interconnected power system is usually extremely large, placing FACTS devices in each transmission line will not be cost-effective. The parameters to be handled will also be very large [11][12][13]. Along with this, maintaining proper coordination between them will not be an easy task too.…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

Gude,

Salma

2022

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This paper proposes a power system stabilizer (PSS) with optimal controller parameters for damping low-frequency power oscillations in the power system. A novel meta-heuristic, weighted grey wolf optimizer (WGWO) has been proposed, it is a variant of the grey wolf optimizer (GWO). The proposed WGWO algorithm has been executed in the selection of controller parameters of a PSS in a multi-area power system. A two-area fourmachine test system has been considered for the performance evaluation of an optimally tuned PSS. A multi-objective function based on system eigenvalues has been minimized for obtained optimal controller parameters. The damping characteristics and eigenvalue location in the proposed approach have been compared with the other state-of-the-art methods, which illustrates the effectiveness of the proposed approach.

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“…There are many researches available in UPFC [15]- [24]. The multiverse optimization is presented in [25] for power sytem oscillation stability. In this paper the PI is tuned to minimize the steady state error of the power in the UPFC control is implemented.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization tuned unified power flow controller for power oscillation reduction

Ananda

Shivakumar²

2021

IJEECS

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One of the best flexible AC transmission system (FACTS) is unified power flow controller (UPFC). As it gets more benefit from both real and reactive power transfer, it is used in power system for controlling the transmitted power. The UPFC controls the power on the transmission side of the power system. When the real as well as reactive power is set the UPFC tries to follow the command by using the proportional and integral (PI) controller. But in some power systems the PI controllers cannot produce the proper power due to the power oscillations. These oscillations are created due to PI controller properties. In this paper the PI controller is replaced with the particle swarm optimization tuned PI controller (PSO-PI). It minimizes the power oscillations by using the objective function. The MATLAB 2017b is used to demonstrate the power transfer curves and the voltages. The IEEE 9 bus system is being used as a reference system.

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Power and energy system oscillation damping using multi-verse optimization

Cited by 17 publications

References 38 publications

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Archives of Electrical Engineering

Particle swarm optimization tuned unified power flow controller for power oscillation reduction

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