Study Of Circuit Subdivision And Stepless Dimming Control Algorithm For Energy Saving On Tunnel Lighting

With the rapid increase in mileage of highway tunnel, resulting in the high operationcosts of a tunnel lighting system based on the maximum illumination design, energy savingbecome an important method to reduce the tunnel lighting energy waste. In engineeringpractice, generally adopted timing-grading dimming control method to achieve the energysaving, this method divide circuit to control lamps logic switching according to the lightingplan of different periods, because it can not combine with real-time weather conditions andtraffic conditions change, caused excess illumination or insufficient illumination, at the sametime, it reduced the security of tunnel passage.This paper gives a energy-saving control model based on exterior environmentluminance, traffic volume and vehicle speed information for the tunnel lighting, meanwhiledesigns a modified circuit subdivision and stepless dimming control algorithm based onhierarchical control. According to predicted data of exterior environment luminance, trafficvolume and speed, combined with ternary parameters energy-saving control model of tunnellighting to calculate the optimized tunnel lighting required luminance, and then according tocircuit subdivision and stepless dimming control algorithm to calculate expected power ofeach sub-circuit lighting dimming lamps, this algorithm simply built the relationship betweentunnel lighting required luminance and expected power of lamps. The computed results showthat: energy saving up to50%and luminance uniformity meet evaluation index of tunnellighting based on circuit subdivision and stepless dimming control algorithm.In this paper, a neural network control method is adopted to simulate circuit subdivisionand stepless dimming control algorithm, a neural network control method for the tunnellighting control system is established with exterior environment luminance, traffic volumeand vehicle speed information as input samples and expected power of lamps dimming astarget output samples. Make use of MATLAB neural network toolbox to train and simulatedimming control algorithm, the experimental result show that: average errors betweenexpected powers and simulation output powers are less than0.2, neural network model fittedwell with the circuit subdivision and stepless dimming control model, and the neural network trained successfully have better generalization ability.