Structural learning approach to replacing unreliable units in a power system

Power supply failure will cause major social loss. Therefore, power supply systems have been required to be highly reliable. This research deals with a significant and effective method to decide the investment in a power system damage of the power supply on the society. In CMD2007 Korea, we proposed mean-variance approach to replacing unreliable units in a power system. This paper extends the method so as to solve a problem efficiently. In this research, we will propose the structural learning of a mutual connective neural network. The proposed method enables us to solve the problem defined in terms of mixed integer quadratic programming. In this research, an analysis is performed by using the concepts of the reliability and risks of units evaluated using a variance-covariance matrix and also the effect and expenses of replacement are measured. Mean-variance analysis is formulated as a mathematical programming with two objectives to minimize the risk and maximize the expected return. Finally, we employ a Boltzmann machine to solve the meanvariance analysis efficiently. The result of our method is exemplified using a power network system in Tokyo Metropolitan. By using this method, a more effective selection of results is obtained. In other words, the decision makers can select the expected investment rate and risk of each ward depending on the given total budget. For this reason, the effectiveness of the decision making process can be enhanced.