TY - JOUR

T1 - Reliability enhancement of power systems through a mean-variance approach

AU - Yaakob, Shamshul Bahar

AU - Watada, Junzo

AU - Takahashi, Tsuguhiro

AU - Okamoto, Tatsuki

PY - 2012/9

Y1 - 2012/9

N2 - Recently, power-supply failures have caused major social losses. Therefore, power-supply systems need to be highly reliable. The objective of this study is to present a significant and effective method of determining a productive investment to protect a power-supply system from damage. In this study, the reliability and risks of each of the units are evaluated with a variance-covariance matrix, and the effects and expenses of replacement are analyzed. The mean-variance analysis is formulated as a mathematical program with the following two objectives: (1) to minimize the risk and (2) to maximize the expected return. Finally, a structural learning model of a mutual connection neural network is proposed to solve problems defined by mixed-integer quadratic programming and is employed in the mean-variance analysis. Our method is applied to a power system network in the Tokyo Metropolitan area. This method enables us to select results more effectively and enhance decision making. In other words, decision-makers can select the investment rate and risk of each ward within a given total budget.

AB - Recently, power-supply failures have caused major social losses. Therefore, power-supply systems need to be highly reliable. The objective of this study is to present a significant and effective method of determining a productive investment to protect a power-supply system from damage. In this study, the reliability and risks of each of the units are evaluated with a variance-covariance matrix, and the effects and expenses of replacement are analyzed. The mean-variance analysis is formulated as a mathematical program with the following two objectives: (1) to minimize the risk and (2) to maximize the expected return. Finally, a structural learning model of a mutual connection neural network is proposed to solve problems defined by mixed-integer quadratic programming and is employed in the mean-variance analysis. Our method is applied to a power system network in the Tokyo Metropolitan area. This method enables us to select results more effectively and enhance decision making. In other words, decision-makers can select the investment rate and risk of each ward within a given total budget.

KW - Boltzmann machine

KW - Mean-variance analysis

KW - Neural network

KW - Power system reliability

UR - http://www.scopus.com/inward/record.url?scp=84865618810&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84865618810&partnerID=8YFLogxK

U2 - 10.1007/s00521-011-0580-z

DO - 10.1007/s00521-011-0580-z

M3 - Article

AN - SCOPUS:84865618810

SN - 0941-0643

VL - 21

SP - 1363

EP - 1373

JO - Neural Computing and Applications

JF - Neural Computing and Applications

IS - 6

ER -