Reliability Evaluation of Bangladesh Power System Using Cumulant Method
Reliability Evaluation of Bangladesh Power System Using Cumulant Method
Nahid-Al-Masood1, M. N. Sahadat2, S.R. Deeba
Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka, Bangladesh E-mail: 1 nahid@eee.buet.ac.bd, 2sahil0406@gmail.com
S.Ahmed1, G.A.K.Biswas, A.U.Elahi, N.M.Zakaria
Department of Electrical and Electronic Engineering BRAC University Dhaka, Bangladesh E-mail: 1shahriar.me@gmail.com weather effects is presented in [6] where the DC-OPF approach is used to determine minimal cut sets (MCS) up to a preset order and then MCSM is used to calculate reliability indices. The appropriate incorporation and presentation of the implications of uncertainty are widely recognized as fundamental components in the analyses of complex systems [7].There are two fundamentally different forms of uncertainty in power system reliability assessment [7, 8]. Aleatory and epistemic uncertainties are considered in power system reliability evaluation in [9] where aleatory uncertainty arises because the study system can potentially behave in many different ways. A method for incorporating the failures due to aging in power system reliability evaluation is presented in [10]. It includes the development of a calculation approach with two possible probability distribution models for unavailability of aging failures and implementation in reliability evaluation. Adverse weather such as hurricanes can have significant impact on power system reliability [11, 12]. One of the challenges of incorporating weather effects in power system reliability evaluation is to assess how adverse weather affects the reliability parameters of system components. A fuzzy inference system (FIS) built by using fuzzy clustering method is combined with the regional weather model to solve the preceding problem is illustrated in [13]. A new computationally efficient methodology for calculating the reliability indices of a bulk power system using the
Nahid-Al-Masood1, M. N. Sahadat2, S.R. Deeba
Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka, Bangladesh E-mail: 1 nahid@eee.buet.ac.bd, 2sahil0406@gmail.com
S.Ahmed1, G.A.K.Biswas, A.U.Elahi, N.M.Zakaria
Department of Electrical and Electronic Engineering BRAC University Dhaka, Bangladesh E-mail: 1shahriar.me@gmail.com weather effects is presented in [6] where the DC-OPF approach is used to determine minimal cut sets (MCS) up to a preset order and then MCSM is used to calculate reliability indices. The appropriate incorporation and presentation of the implications of uncertainty are widely recognized as fundamental components in the analyses of complex systems [7].There are two fundamentally different forms of uncertainty in power system reliability assessment [7, 8]. Aleatory and epistemic uncertainties are considered in power system reliability evaluation in [9] where aleatory uncertainty arises because the study system can potentially behave in many different ways. A method for incorporating the failures due to aging in power system reliability evaluation is presented in [10]. It includes the development of a calculation approach with two possible probability distribution models for unavailability of aging failures and implementation in reliability evaluation. Adverse weather such as hurricanes can have significant impact on power system reliability [11, 12]. One of the challenges of incorporating weather effects in power system reliability evaluation is to assess how adverse weather affects the reliability parameters of system components. A fuzzy inference system (FIS) built by using fuzzy clustering method is combined with the regional weather model to solve the preceding problem is illustrated in [13]. A new computationally efficient methodology for calculating the reliability indices of a bulk power system using the