Design And Implementation Of Fault Diagnosis System For Urban Lighting Based Solar Energy

In the current world,the rapid development of science and technology is actively affecting people's lives.The solar-based street lighting system associated with this has a huge role in achieving urban development and energy conservation.At the same time,although solar-based street lighting systems can save resources by using solar energy,it is generally difficult to maintain in a short period of time if the system fails.In response to these problems,this paper elaborates on the current problems of solar power supply in urban lighting fault diagnosis system,and carried out the following research.First,according to the way solar street lamps work,solar energy is converted into electricity.The proposed development and optimization of a new generation of photovoltaic powered street lighting systems which integrate LEDs devices.The combination of high efficiency photovoltaic panels with whites LEDs of last generation allows the release of an autonomous and performing solar lighting system.The solar street light is used to the nature solar electric.The lighting system is composed to the battery,solar charger controller,lighting sensor control and PV solar panel(necessary size).Secondly,the existing solar based urban public lighting circuit fault diagnosis algorithms are compared.According to the fault characteristics of the lighting control circuit,a circuit fault diagnosis method based on the main component analysis method and the extreme learning machine algorithm is proposed.This proposal can be a lighting fault diagnostic strategy.The voltage RMS sampling of the controller analog circuit of the urban lighting control terminal is taken as the feature sample,the sampling data is extracted and reduced by the principal component analysis method,and the data is fault-classified by the extreme learning machine algorithm to obtain the fault result.At third,the simulated annealing algorithm is used to improve the search ability of global variables.This can overcome the shortcomings of the traditional fault diagnosis core algorithm the traditional extreme learning machine algorithm.Author also proposed a simulated annealing-extreme learning machine algorithm,and used the temperature-based Cauchy to improve fast simulated annealing-extreme learning machine algorithm.Through the comparison test of the accuracy of the sallen-key filter fault diagnosis,this algorithm improves the time characteristics of circuit fault diagnosis experiments.The accuracy of diagnosis is effectively improved.Finally,based on the actual urban public lighting fault diagnosis based on solar energy system is proposed.To ensure the safety of pedestrians and vehicles,this system can automatically diagnosis the faults,and make relevant actions according to the diagnosis results,and also realize the timely response of the faults of the street lamp control terminals.After testing,the system can detect the faults of public lighting control terminal circuit.This system has high fault recognition rate,strong timeliness and accurate fault location.It can meet the requirements of the fault diagnosis system in this period.This system also has good feasibility and practicality.