Average probability of failure on demand estimation for burner management systems

Proper estimation of Safety Integrity Level (SIL) depends largely on accurate estimation of Safety performance in terms of average Probability of Failure on Demand, (PFDavg). For complex architectures of logic solvers, sensors, and valves, this can be calculated by distinguishing combinations of subsystems with basic (K-out-of-N) KooN approach for identical components. In the case of the typical configurations of valves for a burner management systems with non-identical subsystem configurations the KooN approach does not apply. Hence, it becomes an issues to calculate the correct safety performance since some of the established methods give too optimistic results due to lack of Common cause Failure information and data on non-identical components or sub-systems. This paper formulates a Markov model for determination of average probability of failure on demand for non-identical components and also proposes a more conservative lowest failure rate approach and maximum beta factor contrary to pragmatic minimum or average beta for correct estimation of average probability of failure on demand. It can be deduced that the measure of safety performance for components or subsystems with unequal failure rates depends largely on common cause failure, but a single beta factor is not appropriate to model the commonality of the failure. The result revealed that both geometric mean and lowest failure rate approaches result in different values with the lowest failure rate being the most conservative and optimistic result.Keywords: burner management systems, probability of failure on demand, common cause failure, KooN configurations, and lowest failure rate, Markov Analysi

Development of a Hybridized Model for Predicting the Life Span of Power Transformers

Power transformers are important equipment of the electrical switchyard whose failure leads to long hours of outage. In this research, a two-stage hybridized model for determining the lifespan of power transformers is presented by using the furan content to determine the Degree of  Polymerisation (DP) of transformer. For a ‘virgin’ transformer, the furan content was about 0.01ppm while a transformer with about 10ppm was within its end of useful life. 2-Furaldehyde (2FAL) content values of  0.01ppm and 10ppm correspond to DP values of approximately 1200 and 250, respectively. These parameters were used in developing a DP model using Jacobi and Gauss Seidel numerical analysis iterative techniques. The techniques were implemented in Matrix Laboratory 8.2 (R2013b)  environment. The second stage involved the hybridisation of the developed DP model with another rate constant model adopted from Arrhenius. This stage was also implemented in Matrix Laboratory 8.2 (R2013b)  environment. The life span of the transformer was determined by adding the service age at any point in time to the remaining lifetime at that point. A GUI of the hybridised model was developed using SIMULINK blocks. The developed model yielded a DP range of 247 ≤ DP ≤ 1184. Factors such as the hotspot temperature, activation energy and pre-exponential factor were useful for the determination of lifespan.Keywords: Degree of Polymerization, Furan Content, Life Expectancy, Power Transforme

Evaluating the effect of mobility speed on the performance of three handover algorithms in long term evolution networks

This work uses NS3 simulation to study the effect of mobility speed on the performance of three handover algorithms in Long Term Evolution (LTE) Networks. A realistic multi-cell LTE network was set up using NS3 simulation software. Mobility models were used to vary the location of the User Equipment (UE), hence triggering handover events across the network. The performance was measured using Signal Interference Noise Ratio (SINR) and number of completed handovers. Result revealed that at a speed between the ranges of 0 – 3 km/h, the Integrative algorithm performed best while at 4 – 60km/h, the performance of the A3RSRP algorithm was the best with an average value of 95dB. Also, at an increased speed within the range of 60 – 120 km/h, the Integrative algorithm had a slightly better performance than the A3RSRP. However, at a speed above 120 km/h, the integrative algorithm performed best with an SINR of 120dB. In terms of completed handovers, the Integrative algorithm had the least number of completed handovers throughout the entire range of considered speeds. Thus, we establish that mobility speed has a significant effect on the performance of handover algorithms.Keywords: LTE handover, LTE UE Speed, Integrative Algorithm, Power Budget Algorith