بيشينه‌سازي قابليت اطمينان تجهيزات شبكه‌ي انتقال برق از طريق بهينه‌سازي زمان‌‌بندي سرويس تعمير و نگه‌داري با استفاده از الگوريتم مورچگان

Maximization of Electric Power Transmission Grid Reliability through Maintenance Scheduling Optimization using an Ant Colony Algorithm

قابليت اطمينان يكي از شاخص‌هاي مهم در ارزيابي عملكرد و بهره‌برداري از سيستم‌هاي صنعتي است. امروزه راهكارهاي بسياري براي افزايش قابليت اطمينان سيستم‌ها در نظر گرفته مي‌شود. يكي از موثرترين راهكارها انجام سرويس‌هاي تعمير و نگه‌داري پيشگيرانه روي تجهيزات سيستم است. زمان‌‌بندي انجام اين سرويس‌ها غالباً مطابق روش‌هاي كلاسيك با پريودهاي زماني يكنواخت پربسامد يا كم‌بسامد (ماهانه يا سالانه) تعيين مي‌‌شود. در اين تحقيق ابتدا قابليت اطمينان متوسط سيستم باتوجه به انجام سرويس‌هاي تعمير و نگه‌داري پيشگيرانه در بازه زماني پنج‌ساله مدل‌سازي رياضي شده و با استفاده از روش بهينه‌سازي الگوريتم كلوني مورچگان، يك برنامه‌ي زمان‌‌بندي بهينه براي انجام سرويس‌هاي تعمير و نگه‌داري پيشگيرانه به دست آمده تا متوسط قابليت اطمينان سيستم بيشينه ‌شود. سيستم مورد نظر در اين تحقيق تجهيزات يك ايستگاه (پست) نمونه‌ي فوق توزيع برق است.

The problem faced by electric power utilities in developing countries today is that the power demand is increasing rapidly whereas supply growth is constrained by aging generating, transmitting and distributing assets, scarce resources for constructing new ones and other societal issues. This has resulted in the need to construct additional generating plants and for more economic ways to plan and maintain existing electric power transmission and distribution assets. The goal of this study is to improve the average reliability of a system through optimization of its preventive maintenance policy. The evolution of system reliability depends on its structure as well as on the evolution of its components reliability. The latter is a function of component age during a system’s operating life. Component aging is strongly affected by maintenance activities performed on the system. We propose a novel methodology for preventive maintenance policy evaluation, based upon a reliability model, which allows the use of flexible intervals between maintenance interventions. Such innovative features represent an advantage over traditional methodologies, as they allow a continuous fitting of the schedules in order to better deal with the components failure rate. Due to the large amount of parameters to be analyzed and their strong and non-linear interdependencies , the search for an optimum combination of these parameters is a very hard task when dealing with optimization schedules. For this reason, use of an ant colony (ACO) algorithm may be an appropriate optimization technique. In order to demonstrate the effectiveness of the proposed method, it is applied to a typical electric power subtransmission substation. By establishing the failure parameters which will be used, the fault tree is made. Analysis of the sample case fault tree resulted in some minimal cut-sets. Then, we proposed probabilistic modeling. Here, the ant colony modeling allows non constant intervals between maintenance, adapting them to the aging parameter of the Weibull distribution used. The results, when compared with those obtained by some standard maintenance policies, outline its good performance.