A Minimax Polynomial Approximation Objective Function Approach for Optimal Design of Power System Stabilizer by Embedding Particle Swarm Optimization

Soni, Bhanu Pratap; Saxena, Akash; Gupta, Vikas

doi:10.11591/telkomnika.v14i2.7602

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…Design of the SVM has two aspects: the rst is to reduce the number of features by any feature selection method and the second is to nd the largest margin (hyperplane) between both the states of the system (stable and unstable). For handling these aspects, particle swarm optimization (PSO) [37][38][39] is employed in this work. e TSI can be used to assess the stability of the power system and rank the generator.…”

Section: Design Of the Supervised Learning Enginementioning

confidence: 99%

Assessment of Transient Stability through Coherent Machine Identification by Using Least-Square Support Vector Machine

Soni

Saxena

Gupta

et al. 2018

Modelling and Simulation in Engineering

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Transient stability assessment (TSA) of the power system is a crucial issue with escalating demands and large operational constraints. Real-time TSA allows for deciding and monitoring of the relevant preventive/corrective control actions depending on the dynamic behavior of the system components. To assess this, coherency of generating machines is to be found. After determination of the coherent machines, any corrective or preventive action can be initiated by the system operator to maintain stability of the system during occurrence of any severe contingency. e Transient Severity Index (TSI) introduced in this paper has proven to be an interesting alternative for determining generator coherency. Furthermore, the numerical values of this index are employed to construct a supervised learning-based classifier and the ranking method with the help of system load and generation as input features. is framework employs the support vector machine (SVM) to perform the ranking of the generators based on severity and classify them into vulnerable and nonvulnerable machines. e results are validated on the IEEE 10generator, 39-bus test (New England) system. It is observed that the proposed index and the supervised learning engine give satisfactory results and both are aligned with the published approaches.

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Section: Design Of the Supervised Learning Enginementioning

confidence: 99%

Assessment of Transient Stability through Coherent Machine Identification by Using Least-Square Support Vector Machine

Soni

Saxena

Gupta

et al. 2018

Modelling and Simulation in Engineering

Self Cite

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“…In this paper, the researcher had modeled the power curve on SS-CAES prototype with a power capacity 50W by using empirical sampling data output. By using these data, the modeling of the power curve was formed using the regression process, then an equation that represented the power curve was obtained [39]. For validating the results of the power curve modeling in this paper, the modeled data were compared using conventional ways, which was by using mathematical equations and observed data obtained directly from the prototype to determine the accuracy of the proposed method.…”

Section: Modeling Of Power Characteristic Curve On Small Scale Comprementioning

confidence: 99%

Prediction of Power Characteristic Curve on Small Scale Compressed Air Energy Storage by Using Regression Analysis

Widjonarko¹,

Soenoko²,

Wahyudi³

et al. 2018

Preprint

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The research on small scale compressed air energy storage (SS-CAES) becomes an interesting topic especially in optimizing the performance of the system. In this topic, the characteristic curve of the energy storage system is the key to control the system to reach optimum power to the load. In previous research, mathematical equations were used to get the characteristic curve. This paper proposes the polynomial regression based on the actual output data from the prototype to model the characteristic curve of the SS-CAES prototype. The authors have compared the use of mathematical models and polynomial regression in modeling the power curve with actual observational data and determining the level of accuracy of modeling. The results showed that by using polynomial regression, the characteristics of the SS-CAES prototype power curve could only be obtained by using the sample data from the system output with accuracy value 0.967 for R-square. Thus, an approach using this method would facilitate researchers to obtain the characteristics of the curve of the system.

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“…These algorithms require a minimum time to optimize the design parameters of any complex engineering problem when compared with the conventional optimization techniques. Simulated annealing, tabu search, genetic algorithm, PSO, ant‐directed hybrid differential evolution, bacteria foraging algorithm, honey‐bee colony algorithm, harmony search algorithm, cuckoo algorithm, chaotic–teaching‐learning methods, grey wolf algorithm, bat algorithm, water cycle algorithm, backtracking search algorithm, gradient‐based metaheuristic algorithm, whale optimization algorithm, gravitational search agorithm, and minimax polynomial approximation using PSO‐based PSS design techniques are developed by many researchers from the last few years. Though several methods are developed, optimal design of PSS for the highly nonlinear multi‐machine interconnected power system operating at different loading conditions is still essential for robust operation.…”

Section: Introductionmentioning

confidence: 99%

An improved whale optimization algorithm for the design of multi‐machine power system stabilizer

Butti¹,

Mangipudi²,

Rayapudi

2020

Int Trans Electr Energ Syst

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Summary An improved whale optimization algorithm (IWOA) for the design of a robust power system stabilizer (PSS) for the multi‐machine power system is developed in this paper. Tuning of PSS parameters using the proposed IWOA is carried out by minimizing a multi‐objective function comprising the damping ratio and damping factor of lightly damped oscillating modes of all the generators. The advantage of considering the objective function is that the lightly damped and undamped oscillating modes of all the generators can shift to a prespecified zone of the s‐plane. The performance of the proposed IWOA is tested on standard benchmark test functions and compared with familiar optimization algorithms. The efficacy of the proposed design technique is tested on three benchmark test systems: three‐generator nine‐bus system, two‐area four‐machine interconnected system, and the New England 10‐generator 39‐bus system working on various operating conditions under several typical disturbances. The potential of the proposed method is demonstrated through eigenvalue analysis. The proposed IWOA‐based PSS is compared with the other stabilizers to show its efficacy.

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A Minimax Polynomial Approximation Objective Function Approach for Optimal Design of Power System Stabilizer by Embedding Particle Swarm Optimization

Cited by 7 publications

References 14 publications

Assessment of Transient Stability through Coherent Machine Identification by Using Least-Square Support Vector Machine

Assessment of Transient Stability through Coherent Machine Identification by Using Least-Square Support Vector Machine

Prediction of Power Characteristic Curve on Small Scale Compressed Air Energy Storage by Using Regression Analysis

An improved whale optimization algorithm for the design of multi‐machine power system stabilizer

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