Application Of Bacterial Foraging Algorithm In VAV System

With the continuous improvement of living standards, People have a higher requirements for indoor environment. Air conditioning has now become an important part in people’s lives. With a wide range application of air conditioning system, Air conditioning energy conservation has become an outstanding issue, in this situation, energy saving control is very critical for air conditioning system. In the air conditioning system, VAV systems have superior performance in recent years, and has been widespread concerned. AHU is the key equipment in VAV system, it directly affects the quality of operational control and energy efficiency of VAV systems. The main object of this study is AHU, will optimize the control loop in AHU.PID controller is widely used, it is often seen in process control and motion control. It is simply, robustness, and high reliability. PID controller has a lot of advantages, but, when the control object is complex or non-linear, PID controller will has a bad parameter tuning, and can’t reach ideal control effect. Selecting reasonable PID parameters can effectively avoid the above situations, for this purpose, the parameters tuning of PID controller has become a critical part. Bacterial foraging algorithm is a new intelligent algorithm emerged in recent years, it has a higher search speed, can parallel search, and easy to escape from local minima, it has been recognized by scholars. However, the basic bacterial foraging algorithm also has many deficiencies, it has a poor global search ability and a slow convergence speed, thus it need further improvement and refinement in the application process. In this paper, quorum sensing mechanism of the particle swarm algorithm is introduced into the bacterial foraging algorithm, then the bacteria individuals can share information mutually, thus can enhance cooperation between individuals, thereby increasing the speed of global optimization flora.In this paper, PID controller optimized by bacteria foraging algorithm is supplied to the air conditioning unit supply air temperature control loop, and compare with the conventional PID controller, PID controller optimized by bacteria foraging algorithm showed a good performance, and this controller can effectively suppress the interference that outside of the system itself. In this paper, PID controller optimized by particle swarm optimize, PID controller optimized by bacteria foraging algorithm, PID optimized by bacterial foraging algorithm improved by particle swarm optimization were applied to the given hydrostatic control loop in AHU separately, and will has a simulation comparison, comparison chart of PID optimized by particle swarm optimize and PID optimized by bacterial foraging algorithm show that:PID optimized by bacterial foraging algorithm has a smaller overshoot, and has superior performance relative. Comparison chart of PID optimized by bacterial foraging algorithm and PID optimized by bacterial foraging algorithm improved by particle swarm optimization show that:PID optimized by bacterial foraging algorithm improved by particle swarm optimization has short adjusting time, so the algorithm optimized is more reasonable. At the same time, this article will compare the convergence curve of bacterial foraging algorithm with bacterial foraging algorithm improved by particle swarm optimization, the result further indicate that bacterial foraging algorithm improved by particle swarm optimization has a higher convergence speed.