In the era of fossil energy depletion and increasing environmental pollution, clean and renewable new energy represented by photovoltaic power generation has become an increasingly important part of multinational companies’ energy structure. With the advent of the era of photovoltaic parity, the use of photovoltaic tracking systems has become the best choice for many new large-capacity power stations. The cost of the support occupies a very large proportion in the investment of the entire power station construction. Therefore, the rationality of the design of the support, cost control and service life have become important ways for competition in the photovoltaic support industry. Based on the above background, the research content of this article is the application of artificial intelligence algorithms in the safety detection and reinforcement of photovoltaic steel supports. To be able to pass the monitoring data, this paper applies intelligent algorithms to perform faster and more accurate safety inspections on photovoltaic steel supports while minimizing labor costs, and to strengthen the photovoltaic steel supports, this paper chooses neural networks as the basic algorithm A structural model of a photovoltaic steel support was proposed. Finally, experimental simulations showed that the wavelet neural network reached 93.87%. Compared with traditional neural networks, wavelet neural networks perform better in fault prediction accuracy, but the speed needs to be improved. The method proposed in this paper has successfully completed the diagnosis of each component of the photovoltaic bracket in the safety inspection of the photovoltaic steel bracket, and meets the immediateness and accuracy required for the safety inspection of the photovoltaic bracket.
A New Technique for Transmission Loss Allocation in a Deregulated Electricity Market
