Modeling of Consumer Buying Behaviour Using Z-Number Concept

Sadikoglu, Gunay

doi:10.1080/10798587.2017.1327159

Cited by 12 publications

(11 citation statements)

References 9 publications

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“…Also, uncertainty models can be used for parameter estimation. The possibilistic method, Z‐number‐based method and interval‐based method are used as uncertainty models in literature . Time series and fuzzy logic are used in the uncertainty models .…”

Section: Resultsmentioning

confidence: 99%

“…The possibilistic method, Z-number-based method and interval-based method are used as uncertainty models in literature. [40][41][42] Time series and fuzzy logic are used in Abbreviations: ANFIS, adaptive network-based fuzzy inference system; ANN, artificial neural network; DEA, differential evolution algorithm; GA, genetic algorithm; iPSO, inertia weighted particle swarm optimization; PSO, particle swarm optimization. Abbreviations: DEA, differential evolution algorithm; GA, genetic algorithm; iPSO, inertia weighted particle swarm optimization; PSO, particle swarm optimization.…”

Section: Resultsmentioning

confidence: 99%

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High‐voltage cable bonding to prevent cable termination faults based on zero‐sequence current

Akbal

2019

Int Trans Electr Energ Syst

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Summary Zero‐sequence current (ZC) occurs due to harmonics and unbalanced loading current. ZC increases the sheath voltage (SV) and cable temperature, so cable faults occur on cable terminations in high‐voltage underground cable lines (HVUCL). In literature, bonding methods are used to prevent ZC effects, but these methods are not adequate to prevent ZC effects for long HVUCL under high harmonic distortion because ZC is not considered when these methods are designed. In this study, the modified sectional solid bonding (MSSB) is suggested for long HVUCL that works under high harmonic distortion rate and unbalanced loading conditions. Namely, ZC is considered when MSSB is designed. MSSB parameters should be determined before HVUCL is installed for the most economical and practical bonding. SV of HVUCL should be known to determine MSSB parameters, so SV of HVUCL is forecasted by using adaptive network‐based fuzzy inference system (ANFIS), artificial neural networks (ANN), and hybrid ANN. Training and forecasting errors of hybrid ANN are less than ANFIS and ANN methods, so hybrid ANN methods are suggested to forecast SV of HVUCL. Also, MSSB parameters should be optimized, so optimization methods are used for optimization of MSSB. When MSSB method is used for bonding of long HVUCL, harmonic distortion rate and SV in cable terminations are not exceeded the determined limits to prevent cable faults.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

High‐voltage cable bonding to prevent cable termination faults based on zero‐sequence current

Akbal

2019

Int Trans Electr Energ Syst

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“…For example, the output power from a wind turbine varies according to the wind speed at the site [39]. Given a wind regime, the available power output from a wind turbine can be used according to its operational characteristics [40], [41] and [42] introduce methods to handle the uncertainty issue. In this study, the uncertainty of the power generation from the DG units is neglected for simplicity and constant models of the DG units are used instead as a sample of a typical winter day.…”

Section: Opf With the Dg Unitsmentioning

confidence: 99%

Optimal Power Flow of Power Systems Including Distributed Generation Units Using Sunflower Optimization Algorithm

et al. 2019

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This article introduces a new attempt of utilizing the sunflower optimization (SFO) algorithm in solving the problem of optimal power flow (OPF) in the field of power systems. The principle target is to optimize the generating units' fuel cost under the system constraints. At initial stage, the objective function is solved to find the optimal siting of Distributed Generation (DG) units within the system under study. Then, different scenarios are performed to solve the OPF problem including and excluding DG units. The generators' real output power defines the exploration field for the OPF problem. The SFO algorithm is used to minimize the fitness function and yields the best solutions of the problem. More than one electric grid is tested to check the validity of the proposed algorithm such as the IEEE 14-bus, and 30-bus networks. Simulations included different scenarios are implemented in these two networks. To obtain a realistic result, real daily load curve is considered in this study. The results of simulations are investigated and analyzed. Results confirm the flexibility, validation, and applicability of the introduced SFO-based OPF methodology when compared with the genetic algorithm. INDEX TERMS Optimal power flow, optimization, power systems, renewable energy.

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“…The uncertainties in TNEP due to consumer behaviour and decision-making process are subject to influences of social-cultural and psychological as well as personal factors. The work of Sadikoglu [50] utilized Z-number concept in handling uncertainties in consumer buying behaviour analysis. Moreover, a hybrid probabilistic-possibilistic model was developed in [51], to indicate the dependency of customer performance with demand response(DR) operations by the use of two defined utility-based indices.…”

Section: Uncertainty Issues In Tnepmentioning

confidence: 99%

A Comprehensive State-of-the-Art Survey on the Transmission Network Expansion Planning Optimization Algorithms

2019

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Long term planning in power transmission network expansion provides a well ordered and profitable extension of power equipment and facilities to meet the expected electric energy demand with an allowable degree of reliability. However, high quality and improved reliability in energy supply have to be balanced with the available funds. The need to expand transmission network can never be over emphasized. Transmission Network Expansion Planning (TNEP) is a periodical measure that must be carried out due to dynamic societies that attract extra energy demands. It is highly important to minimize the network reinforcement and operational costs while satisfying the increase in demand imposed by technical and economic conditions over the planning horizon. Several optimization algorithms for TNEP problems have been developed and applied over the past decades. This paper presents a comprehensive state-of-the-art survey on the TNEP optimization algorithms. The approach of this paper is in the area of highlights of the various available TNEP algorithms, their applications, viability, computational complexities and drawbacks, which can aid in the identifications of the proper methods that can yield an optimal solution to TNEP problem. INDEX TERMS Algorithm, hybrid, meta-heuristics, optimization techniques, power network expansion planning, power system, transmission network expansion.

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Modeling of Consumer Buying Behaviour Using Z-Number Concept

Cited by 12 publications

References 9 publications

High‐voltage cable bonding to prevent cable termination faults based on zero‐sequence current

High‐voltage cable bonding to prevent cable termination faults based on zero‐sequence current

Optimal Power Flow of Power Systems Including Distributed Generation Units Using Sunflower Optimization Algorithm

A Comprehensive State-of-the-Art Survey on the Transmission Network Expansion Planning Optimization Algorithms

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