Abstract
In order to avoid the nuclear accidents during the operation of nuclear power plants, it is necessary to always monitor the status of relevant facilities and equipment. The premise of condition monitoring is that the sensor can provide sufficient and accurate operating parameters. Therefore, the sensor arrangement must be rationalized.
As one of the nuclear auxiliary systems, the chemical and volume control system plays an important role in ensuring the safe operation of nuclear power plants. There are plenty of sensor measuring points arranged in the chemical and volume control system. These sensors are not only for detecting faults, but also for running and controlling services.
Particle swarm algorithm has many applications in solving the problem of sensor layout optimization but the disadvantage of the basic particle swarm optimization algorithm is that the parameters are fixed, the particles are single, and it is easy to fall into the local optimization. In this paper, the basic particle swarm optimization algorithm is improved by Non-linearly adjusting inertia weight factor, asynchronously changing learning factor, and variating particle. The improved particle swarm optimization algorithm is used to optimize the sensor placement.
The numerical analysis verified that a smaller number of sensors can meet the fault detection requirements of the chemical and volume control system in this paper, and Experiments have proved that the improved particle swarm algorithm can improve the basic particle swarm algorithm, which is easy to fall into the shortcomings of local optimization and single particles. This method has good applicability, and could be also used to optimize other systems with sufficient parameters and consistent objective function.
Quantum-behaved particle swarm optimization-based active noise control system with timing varying path
