Central air-conditioning parallel chiller load optimization distribution method based on swarm intelligence architecture

Abstract

The invention discloses a central air conditioner parallel refrigerator load optimal distribution method based on a swarm intelligent framework. A controller is arranged in each refrigerator in a parallel refrigerator system, and all controllers in the parallel refrigerator system are connected according to an actual physical topology connection relationship of the refrigerators to form a swarm intelligent network system; when one certain controller initiates an adjustment task, the remaining controllers cooperate with the controller initiating the adjustment task to perform information interaction with the respective neighbor controllers, response system operation is completed in allusion to the adjustment task, and autonomous coordination adjusting operation of the parallel refrigerator system is achieved; and a distributed chaos distribution estimation method is used for conducting optimized calculation on an objective function to complete an optimal assignment task. The central air conditioner parallel refrigerator load optimal distribution method improves the working efficiency, reduces system energy consumption and saves energy.

Description

technical field [0001] The invention belongs to the technical field of air-conditioning and refrigeration, and in particular relates to a method for optimizing load distribution of central air-conditioning parallel refrigerators based on a group intelligence architecture. Background technique [0002] The central air-conditioning system has always been a large energy consumer in buildings, so it is of great significance to study the energy-saving optimization of the central air-conditioning system. At present, the operation of the parallel chillers in the central air-conditioning system is often regulated by the operation and maintenance personnel according to the weather and their own experience. The automation level is low, and it will cause more energy waste. The parallel cooling machine operation control strategy used in the actual system is also based on the preset threshold value to realize the strategy of adding and subtracting machines, so as to adapt to the change o...

AI Technical Summary

Problems solved by technology

[0005] Based on the swarm intelligence architecture, the central air-conditioning parallel chiller load optimal distribution method, each chiller in the parallel chiller system is equipped with a controller, and all controllers in the parallel chiller system are interconnected according to the actual physical topology connection relationship to form Group intelligent network system; when a certain controller initiates an adjustment task, the other controllers cooperate with the initiating controller to exchange information with their neighbor controllers, and complete the corresponding system calculation for the adjustment task, realizing the autonomous coordination of the parallel chiller system Adjust the operation; calculate the power consumption and cooling capacity under different load rates according to the chiller load rate-power performance curve parameters and the rated cooling capacity parameters; then each controller obtains the total value of the parallel chiller system under different load rate combinations Cooling capacity and total power consumption; calculate the fitness value according to the fitness function and perform chaotic mutation operation; eliminate some individuals with poor fitness as the parent population and use the statistical analysis method to obtain the sample mean and variance of the Gaussian distribution, and use this The Gaussian distribution model generates a new population; the new population and the parent population are used as a population to recalculate the fitness value, and some individuals with poor fitness in the population are eliminated to become the offspring population; so far, an evolutionary process is completed, and the offspring population follows When the parallel chiller system satisfies the terminal load demand, evolve one generation in the direction of the minimum energy consumption, and continue to evolve until the sample variance obtained by statistical analysis is less than the accuracy requirement or reaches the maximum evolutionary number, then combine the load rate with the greatest fitness in the last generation population As a result of optimal adjustment, each chiller is controlled by the controller to complete the adjustment process and complete the optimal distribution

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