The crux of reliability engineering is to analyze the causes of failures, identify the consequences of failures and building of reliable systems by utilizing reliability basic design concepts. Optimal reliability level of the power utility depends on the constraints such as cost factors, available resources, economic benefits and profitability of such exercises. Thus optimization techniques have to be applied while designing reliable systems. This paper presents an approach based on Latin Hypercube Sampling with state adequacy analysis using Generalized Regression Neural Network (GRNN), and Particle Swarm Optimization (PSO) for determining the optimal reliability parameters of the composite generation and transmission system. The cost-benefit based design model has been formulated as an optimization problem of minimizing the system interruption cost and the component investment cost. The design model requires the analysis of several reliability levels which need to evaluate Expected Demand Not Supplied (EDNS) index for those levels. This approach reduces the computational burden for EDNS evaluation by applying GRNN for state adequacy analysis of the sampled states. The optimal reliability design model which has non-linear objective function and constraints is solved using PSO algorithm. Case studies carried out for Modified Stagg & El-Abiad 5-bus system and IEEE 14-bus system.

Reliability design of composite generation and transmission system based on latin hypercube sampling with GRNN state adequacy evaluation

This research paper analysed the influence of aerodynamic disturbance on vehicle’s dynamic parameters. The vehicle dynamics were formulated from the Newton’s Second Law for modelling the vehicle. The vehicle was built using rigid body frames, mass and multi-body signal blocks of MapleSim2015 platform. Several vehicle masses were used to produce different vehicle dynamics with respect to the same aerodynamic drag and input force. Our analyses have shown that the mass of each vehicle is inversely proportional to the aerodynamic drag applied to it. At a given set-point of 25 ms-1, the vehicle tracked the given speed exactly in the absence of the drag. However, for the lag in displacement, speed and acceleration were found as 25 m, 17 ms-1 and 0.3 ms-2, respectively in the presence of drag with an average jerk of 45 ms-3. This has provided an interesting insight on the effects of drag on the moving vehicle. The proposed vehicle was subjected to the same control strategy to form a two-vehicle, look-ahead convoy as in conventional type. Improvements in the inter-vehicular spacing of 1.7 m, proper speed track, low acceleration(1.05 ms-2) and a suitable jerk of 0.04 ms-3 were achieved over the entire period (160 s) as compared to conventional vehicle. The proposed vehicle model scores higher accuracy than conventional vehicle on two-vehicle, look-ahead model and it has shown that the proposed model is more comfortable than the conventional one.

Aerodynamic disturbance on vehicle’s dynamic parameters

Mobility models, which determine the movement pattern of nodes communicating wirelessly, play a vital role in the simulation-based evaluation of Vehicular Ad Hoc Networks. Realistic mobility models enable us to assess the network performance more accurately, and it is required to design a realistic mobility model that mimics the real vehicle scenario. This paper made an attempt to bring out a suitable mobility model for vehicular networks, which improves the network performance in accordance with string stability, by incorporating the concept of Fuzzy Cooperative Adaptive Cruise Control mechanism among a platoon of vehicles. The experimental study and analysis find that fuzziness improves the performance of the network in terms of packet delay ratio, delay and throughput.

K. Komathy

Papers

Performance analysis of fuzzy co-operative adaptive cruise controller in vehicular ad hoc networks