Synergistic optimization control method and system for biogas internal combustion generator set

Abstract

The invention discloses a collaborative optimization control method and system for a biogas internal combustion engine generator set. An overall operation model of the biogas internal combustion engine generator set is established based on a deep belief network (DBN), a particle swarm optimization algorithm is used for solving a collaborative optimization control model of the biogas internal combustion engine generator set, and finally the optimal collaborative optimization air-fuel ratio parameters and gas intake volume are output to achieve the collaborative optimization control over the biogas internal combustion engine generator set. The power generation efficiency of the biogas internal combustion engine generator set can be improved, the voltage output stability and the fast load tracking ability of the generator set can be improved, more importantly, the safe, stable and reliable operation of the combined cooling heating and power system and the national grid is ensured, and thecollaborative optimization control method and system for the biogas internal combustion engine generator set have great significance.

Description

technical field [0001] The invention relates to the technical field of biogas power generation, in particular to a method and system for synergistic optimization control of biogas internal combustion generator sets. Background technique [0002] Energy shortage, environmental pollution and climate change are important factors restricting the sustainable development of the world economy and society. Energy and environmental issues have become major strategic issues of great concern both at home and abroad. The biogas internal combustion generator set is a new energy comprehensive utilization system integrating environmental protection and energy saving. It uses a large amount of biogas produced by anaerobic fermentation of a large amount of organic waste in industry, agriculture or urban life. Thermal energy drives the internal combustion generator set to generate electricity, and can make full use of the waste heat of the generator set to realize the combined cooling, heatin...

AI Technical Summary

Problems solved by technology

[0003] However, the concentration of biogas methane produced by different organic waste raw materials and different fermentation temperatures has large differences and fluctuations, and it is difficult to establish a dynamic mathematical model that accurately describes the operating characteristics of biogas internal combustion generator sets under different biogas components and multiple working conditions. Insight into its complex operation rules makes it difficult for biogas internal combustion generators to operate in the high-efficiency zone. Therefore, it is quite challenging to achieve optimal control of biogas internal combustion generators

At the same time, most of the existing biogas internal combustion generator sets are refitted on the basis of natural gas internal combustion generator sets. The parameters of the control system are not optimized and automatically adjusted in real time according to the biogas composition, load rate, and operating conditions, resulting in the voltage of biogas internal combustion generator sets. Technical problems such as large output fluctuations, slow response to load tracking, and low power generation efficiency

The problem in the existing technology is that the air-fuel ratio control system and the speed control system in the current biogas internal combustion generator set are controlled by independent controllers, and there is no cooperative optimization mechanism between the two, and there is no coordinated optimization control of biogas as a whole. High-efficiency operation of the internal combustion generator set, thereby improving the voltage output stability and power generation efficiency of the biogas internal combustion generator set

However, none of the existing biogas internal combustion generator sets involves the overall modeling of the biogas internal combustion generator set, the collaborative optimization control method and system of the air-fuel ratio control system and the speed control system

Images