Reliability optimization using ant colony algorithm under performance and cost constraints

• Published in: Electric power systems research Vol. 76; no. 1; pp. 1 - 8
• Main Authors: Meziane, R., Massim, Y., Zeblah, A., Ghoraf, A., Rahli, R.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.09.2005; Elsevier
• Subjects: Ant colony algorithm (ACA); Applied sciences; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Multi-state system (MSS); Operation. Load control. Reliability; Power networks and lines; Reliability optimization; Universal generating moment function (UMGF); UMGF; ROP; Reliability optimization; Ant colony algorithm (ACA); Universal generating moment function (UMGF); MSS; Multi-state system (MSS); ACA; Production system; Generating function; Optimization method; System status; Multi-state system; System reliability; Implementation; Optimal design; Heuristic programming; Series parallel connection; Experimental result; System performance; Computer program; Electrical network; Numerical simulation; Cost estimation; Heuristic approach; Power system reliability; Electric device
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2005.02.008

Reliability engineers often try to achieve a high reliability level of production systems. The problem of electrical network reliability optimization where redundant electrical devices are included is considered. The system reliability maximization subject to performance and cost constraints is well known as reliability optimization problem. In reality, the system has a range of performance levels, in this case a multi-state system (MSS) reliability is defined as the ability to maintain a specified performance level. A procedure, which determines the maximal reliability of series–parallel electrical power system topology is proposed. In this procedure, electrical system devices are chosen among a list of available devices on the market. Electrical devices are characterized by their reliability, performance and cost. To evaluate the systems reliability, a universal moment generating function (UMGF) approach is used by the ant colony algorithm (ACA) to determine the optimal electrical power network topology. A computer program has been developed to implement the UMGF technique combined with the ant colony algorithm. An illustrative example is treated at the end of this work.