الفهرس | Only 14 pages are availabe for public view |
Abstract In the recent years, the rapid progress of digital technology to safety-critical systems such as the Reactor Protection System (RPS) is increased. The RPS is one of the most important digital Instrumentation and Control (I&C) systems utilized in Nuclear Power Plants (NPPs) for safe operation and shut-down of the reactor in emergency events. It ensures a safe reactor trip when the safetyrelated parameters violate the operational limits and conditions of the reactor. Reliability assessment of RPS is an essential issue to maintain a high degree of reactor safety and cost savings. Achieving high reliability and availability of RPS ensures that the NPPs able to perform required tasks under given conditions over a time interval. This work aims to perform reliability analysis and improvement of the RPS; this is done through three new suggested models. First, a quantitative evaluation reliability analysis model for the RPS with 2-outof-4 architecture using the state transition diagram is proposed. The model assesses the effects of independent hardware failures, Common Cause Failures (CCFs), and software failures on the RPS failure through calculating the Probability of Failure on Demand (PFD) which characterizes the safety of the RPS. The effectiveness of the suggested model is verified by comparing the obtained results with that of the Fault Tree Analysis (FTA) technique. Second, a general methodology for improving reliability of the RPS in NPP based on a Bayesian Belief Network (BBN) is suggested. The structure of the BBN model is based on the incorporation of failure probability and downtime of the RPS I&C modules. Various architectures with dual state nodes for the RPS I&C components are developed for reliability sensitive analysis and to demonstrate the effect of RPS I&C modules on the failure of the entire system. A reliability framework clarified as a Reliability Block Diagram (RBD)transformed into a BBN representation is constructed for each architecture to identify which one will fit the required reliability. Two common component importance measures are applied to define the impact of RPS I&C modules, which revealed that some modules are more risky than others and have a larger effect on the failure of the RPS. Third, a hybrid machine learning reliability evaluation model of the nuclear RPS in NPP is proposed. Initially, the significant reliability factors in RPS are classified to four sections: Interlocks, over-power DT-shutdown, overtemperature DT-shutdown, and reactor shutdown logic circuit. Each section represents a subsystem of the integrated RPS safety system. Feature selection strategy based on Principal Component Analysis (PCA) is applied for low impact factors exclusion. Then, the Support Vector Regression (SVR) algorithm is utilized to promote machine learning models for the reliability evaluation of each subsystem. Particle Swarm Optimization (PSO) is applied for each learning model for parameter optimization of SVR. Thereafter, Artificial Neural Network (ANN) is employed to correlate the reliability of the four subsystems with the reliability of the whole RPS with utilizing PSO for an optimization process to minimize the number of training phases. At last, residual error Correction of Markov chain is adapted to improve the predictive performance of the proposed learning model. In comparison with some various literature methods, the achieved results demonstrate the validity and effectiveness of the proposed work for reliability evaluation and improvement of the RPS. Keywords: Reactor Protection System (RPS), Nuclear Power Plant (NPP), State Transition Diagram, Probability of Failure on Demand (PFD), Bayesian Belief Network (BBN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Particle Swarm Optimization (PSO). |