Hybrid bio-Inspired computational intelligence techniques for solving power system optimization problems: A comprehensive survey

Rahman, Imran; Mohamad–Saleh, Junita

doi:10.1016/j.asoc.2018.04.051

Cited by 53 publications

(17 citation statements)

References 192 publications

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“…Furthermore, many intelligent methods have been developed to find out the optimal solution for various technical problems [19], [20], [40]- [46]. One of them is the ANN considered the backpropagation.…”

Section: Problem Statementmentioning

confidence: 99%

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Yuan¹,

Afandi²,

Putranto³

2018

EECSI

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Presently, electrical energy consumption continues to increase from year to year. Therefore, a short-term load forecasting is required that electricity providers can deliver continuous electrical energy to electricity consumers. By considering the estimation of the electrical load, the scheduling plan for operation and allocation of reserves can be managed well by the supply side. This study is focused on a forecasting of electrical loads using Artificial Neural Network (ANN) method considering a backpropagation algorithm model. The advantage of this method is to forecast the electrical load in accordance with patterns of past loads that have been taught. The data used for the learning is Actual Peak Load Period (APLP) data on the 150 kV system during 2017. Results show that the best network architecture is structured for the APLP Day and Night. Moreover, the momentum setting and understanding rate are 0.85 and 0.1 for the APLP Day. In contrast, 0.9 and 0.15 belong to the APLP Night. Based on the best network architecture, the APLP day testing process generates Mean Squared Error (MSE) around 0.04 and Mean Absolute Percentage Error (MAPE) around 4.66%, while the APLP Night generates MSE in 0.16 and MAPE in 16.83%.

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Section: Problem Statementmentioning

confidence: 99%

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Yuan¹,

Afandi²,

Putranto³

2018

EECSI

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“…Firstly, tuning CPSS parameters to optimize its performance using Meta-heuristic algorithms as in Strength Pareto Evolutionary Algorithm [8], cultural algorithms [9], Quasi-oppositional symbiotic organism search (QOSS) [10], & Salp Swarm Algorithm [11], moreover, artificial intelligent techniques as the BAT optimization algorithm (BATOA) [12], Genetic Programming [13], ant lion's optimizer [14], and Bioinspired Algorithms [1,15].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers found some lacks in GA performance, which looked at the application with greatly epistatic objective functions. Also, the hasty convergence of GA reduces its act and decreases the search capability [13,15] Secondly, PSS design, by robust & evolutionary control techniques as H∞ control [16,17], quantitative feedback theory [18], & sliding mode [19]. Thirdly, researchers work to enhance PSS performance by changing its structure, optimal and a suboptimal power system stabilizer [20], fractional-order proportionalintegral-differential (FOPID) controller [21], multi-band PSS [7,22].…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of Bacterial Foraging based Power System Stabilizer in Multi-Machine Power System

Ibrahim¹,

Elnaghi²,

Ibrahim³

et al. 2019

IJISA

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“…The determination of optimal DG location and/or size, the current trend is to invent reliable and robust technique that can alleviate the current setback experienced in the current techniques [14][15][16][17]. Apart from finding higher probability of optimum solution towards the objective functions, new optimization algorithms are also developed to alleviate computational burden in the classical optimization techniques [18].…”

Section: Introductionmentioning

confidence: 99%

Effect of Optimal Multi-DG Siting and Sizing in Transmission System using Hybrid Optimization Technique for Loss Control

Shaaya¹,

Musirin

Sulaiman

et al. 2019

IJEAT

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The advancement of Distributed Generation (DG) technologies have caused great impact to power system operation. The installation of DG has been expanded in both the transmission and distribution systems. Improper planning of DGs installation may lead to over-compensation or undercompensation. Thus, a reliable optimization is urgent to alleviate any undesirable event. This paper analyses the impact of multiDGs installation utilizing a pre-developed hybrid optimization technique termed as Immunized-Brainstorm-Evolutionary Programming (IBSEP). It is imperative to study the effect of multi-DGs installation such that a relevant utility can make a correct decision, whether its installation is worth or vice versa. Rigorous study has been conducted in terms of identifying the optimal location and sizing, installed on transmission system for loss control involving different DG types. Comprehensive results embedded in this paper show that more optimal DGs of a particular type are superior to the other in controlling transmission system loss.

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Hybrid bio-Inspired computational intelligence techniques for solving power system optimization problems: A comprehensive survey

Cited by 53 publications

References 192 publications

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Performance Assessment of Bacterial Foraging based Power System Stabilizer in Multi-Machine Power System

Effect of Optimal Multi-DG Siting and Sizing in Transmission System using Hybrid Optimization Technique for Loss Control

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