Optimal Power Scheduling Strategy in Power Systems using Swarm Optimization Technique

Kamari, Nor Azwan Mohamed; Rahmat, Nur Ezan; Musirin, Ismail

doi:10.30534/ijatcse/2019/3781.62019

Cited by 5 publications

(8 citation statements)

References 6 publications

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“…In this paper, the performance of BA and EP are conducted according to two types of systems: IEEE standards of nine bus with three generator system (System 1) and thirty bus with six generator system (System 2) [5].…”

Section: Results and Analysismentioning

confidence: 99%

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Optimal Energy Scheduling Strategy in Power System using Bat Algorithm Optimization Approach

2020

IJETER

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This paper presents an energy scheduling strategy for power system using Bat Algorithm (BA) optimization technique. BA is inspired by the natural behaviour of microbats, that of echolocation. The paper focuses on the minimization of the total power and cost needed according to the power demand and limits of power generation. Two systems, the IEEE 9 bus with 3 generator system and IEEE 30 bus with 6 generator system, are studied and simulated using Matlab. The performance of BA is compared with evolutionary programming (EP) technique. The simulation results indicate that BA performs better than EP in determining the optimal power generation valuewith minimum generation cost.

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Section: Results and Analysismentioning

confidence: 99%

“…Economic dispatch (ED) is an important key to consider in the development of the power system. It has been widely used in power system designs to minimize the cost while achieving the power demand [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Scheduling Strategy in Power System using Bat Algorithm Optimization Approach

2020

IJETER

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“…Different areas of wireless communications will benefit immensely from this work especially the areas of queuing and modeling fading channels. Furthermore, different evolutionary computational methods can be applied in lieu of the differential evolution [60][61][62][63][64].…”

Section: Discussionmentioning

confidence: 99%

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Okagbue¹

2020

IJATCSE

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Erlang distribution is a particular case of the gamma distribution and is often used in modeling queues, traffic congestion in wireless sensor networks, cell residence duration and finding the optimal queueing model to reduce the probability of blocking. The application is limited because of the unavailability of closed-form expression for the quantile (inverse cumulative distribution) function of the distribution. The problem is primarily tackled using approximation since the inversion method cannot be applied. This paper extended a six parameter quantile model earlier proposed to the Nakagami distribution to the Erlang distributions. Consequently, the established relationship between the two distributions is now extended to their quantile functions. The quantile model was used to fit the machine (R software) values with their corresponding quartiles in two ways. Firstly, artificial neural network (ANN) was used to establish that a curve fitting can be achieved. Lastly, differential evolution (DE) algorithm was used to minimize the errors obtained from the curve fitting and hence estimate the values of the six parameters of the quantile model that will ensure the best possible fit, for different values of the parameters that characterize Erlang distribution. Hence, the problem is constrained optimization in nature and the DE algorithm was able to find the different values of the parameters of the quantile model. The simulation result corroborates theoretical findings. The work is a welcome result for the quest for a universal quantile model that can be applied to different distributions.

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“…In addition, Poli has also done a comprehensive survey of PSO applications. A comprehensive review of theoretical and experimental work on PSO was published recently by reference [14].…”

Section: An Adaptive Handover Initiation Thresholdfor Seamless Mobility Basedwireless Networkusing Particle Swarm Optimization (Pso) Algomentioning

confidence: 99%

An Adaptive Handover Initiation Thresholdfor Seamless Mobility basedWireless Networksusing Particle Swarm Optimization (PSO) Algorithm

Yusof¹

2020

IJATCSE

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In the near future mobility management, handover initiation procedure is important as it will become a necessity trend. The major problem related to mobility performance is the time required for the network to make the decision of handover. A poorly designed handover process tends to make more cases of data loss or radio link failure and thus there must be an optimum handover threshold value to ensure a seamless handover process from serving to the target base station.Throughout this work, a system for analytical handover with mathematical equations was developed and derived by using MATLAB software. This research has proposed An adaptive Reference Signal Received Power Threshold (RSRPth)to initiate the handover process by using the user's speed and the handover signaling delay value.probability of handover failure (p f ) was analyze based on the derivation of the adaptive value of RSRP th . From the obtained result, it has been found that with the developed analytical handover framework, the proposed adaptive RSRP th value for handover initiation has improved the number of handover failures.

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Optimal Power Scheduling Strategy in Power Systems using Swarm Optimization Technique

Cited by 5 publications

References 6 publications

Optimal Energy Scheduling Strategy in Power System using Bat Algorithm Optimization Approach

Optimal Energy Scheduling Strategy in Power System using Bat Algorithm Optimization Approach

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

An Adaptive Handover Initiation Thresholdfor Seamless Mobility basedWireless Networksusing Particle Swarm Optimization (PSO) Algorithm

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