Estimation of Synchronizing and Damping Torque Coefficients Using Deep Learning

Hammoudeh, Ahmad; Saaideh, Mohammad Al; Feilat, Eyad A.; Mubarak, Hamza

doi:10.1109/jeeit.2019.8717432

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…LS technique is often used to obtain the minimum value for the sum of the square of the differences between ΔTe(t) and ΔTes(t). The error is defined as [8][9][10]:…”

Section: Ls Methodsmentioning

confidence: 99%

“…This analysis is used to predict electromagnetic swing at low frequencies as a result of undisturbed rotor swing. References [1][2][3][4][5][6][7][8][9][10] pointed out that the stability of the oscillation in the power system is an important issue. As the power system operation changes over time, the stability of the small signal in this power system should be tracked online.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, damping torque coefficient Kd and synchronizing torque coefficient Ks are used as stability indicators. Both Ks and Kd values must be positive so that the system can be classified as stable [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The least squares (LS) method is a technique commonly used to find the Ks and Kd values, as reported in [8][9][10]. However, data update requirements are a major weakness of the LS method.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent swarm-based optimization technique for oscillatory stability assessment in power system

Kamari

Musirin

Ibrahim

et al. 2019

IJ-AI

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This paper discussed the prediction of oscillatory stability condition of the power system using a particle swarm optimization(PSO) technique. Indicators namely synchronizing(Ks)and damping(Kd) torque coefficients is appointed to justify the angle stability condition in a multi-machine system. PSO is proposed and implemented to accelerate the determination of angle stability. The proposed algorithm has been confirmed to be more accurate with lower computation time compared with evolutionary programming(EP) technique. This result also supported with other indicators such as eigenvalues determination, damping ratio and least squares method. As a result, proposed technique is achievable to determine the oscillatory stability problems.

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“…LS technique is often used to obtain the minimum value for the sum of the square of the differences between ΔTe(t) and ΔTes(t). The error is defined as [8][9][10]:…”

Section: Ls Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent swarm-based optimization technique for oscillatory stability assessment in power system

Kamari

Musirin

Ibrahim

et al. 2019

IJ-AI

View full text Add to dashboard Cite

show abstract

“…K s and K d can be calculated on the basis of the information from three rotor responses, namely the changes in rotor angle, ∆δ(t); rotor speed, ∆ω(t); and electromechanical torque, ∆T e (t). A system is considered stable when the values of K s and K d are positive [10][11][12]. However, the system is considered unstable when one of the values is negative.…”

Section: Introductionmentioning

confidence: 99%

PSO-Based Oscillatory Stability Assessment by Using the Torque Coefficients for SMIB

et al. 2020

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This study discusses the evaluation of oscillatory stability based on the synchronizing K s and damping K d torque coefficients for a single-machine system connected to an infinite bus (SMIB). Particle swarm optimization (PSO) technique is used to determine K s and K d values and subsequently identify the oscillatory stability conditions in the SMIB. The ability of PSO is compared with those of evolutionary programming (EP) techniques and artificial immune system (AIS). The least square (LS) method is selected as the benchmark for K s and K d values determined by PSO, EP, and AIS. Simulation results show that PSO successfully estimated K s and K d values closest to LS compared with EP and AIS. PSO also uses lower computational time compared with those of the two other techniques. The proposed technique is suitable for solving oscillatory stability problems based on the determination of eigenvalues and minimum damping ratio.Energies 2020, 13, 1231 2 of 15 K d values. Thus, heuristic techniques are introduced to solve this problem. K s and K d values will be optimized from the beginning of the data until constant K s and K d values are obtained. Therefore, only a portion of the data is necessary for estimating K s and K d . Minor data errors do not significantly affect the determination of K s and K d values.In recent years, the use of artificial intelligence (AI) technology has become the preferred option in solving power system problems. The use of AI is introduced to solve the optimum values of a system or condition particularly in the fields of economic dispatch, capacitor placement and sizing, and assessment and improvement of voltage and oscillatory stability. Artificial neural networks [16,17], evolutionary programming (EP) [18][19][20], artificial immune systems (AIS) [21][22][23], and ant colony optimization (ACO) [24][25][26] are AI approaches that are commonly used in power systems. The EP algorithm is modeled on the biological evolution process of solving a complex problem. The main features of EP include the mutation process of the next generation and the selection of increasingly powerful genes. The AIS algorithm uses a concept similar to that of EP. Although both concepts are biologically based on living things, EP focuses on the evolution of living things, whereas AIS adopts the concept of the living immune system. The difference between AIS and EP algorithms is that AIS has an additional process of cloning called the clonal selection algorithm. However, the ACO approach is inspired by the true behavior of ants while searching for food and interacting with fellow ants. In ACO, artificial ants (the search agent) will communicate by using pheromones, which guide the searcher ants to solve the calculation problem by tracking the best route. Meanwhile, the particle swarm optimization (PSO) [27][28][29][30] concept mimics the movements of a herd, such as the behavior of schooling fish and swarming insects. This technique was originally founded based on the population of random particles, in which every p...

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Electricity Price Forecasting One Day Ahead by Employing Hybrid Deep Learning Model

Abdellatif

Mubarak

Ahmad

et al. 2023

2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)

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Estimation of Synchronizing and Damping Torque Coefficients Using Deep Learning

Cited by 7 publications

References 14 publications

Intelligent swarm-based optimization technique for oscillatory stability assessment in power system

Intelligent swarm-based optimization technique for oscillatory stability assessment in power system

PSO-Based Oscillatory Stability Assessment by Using the Torque Coefficients for SMIB

Electricity Price Forecasting One Day Ahead by Employing Hybrid Deep Learning Model

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