Nuclear Power Plants are a complex system and need to be controlled very meticulously to avoid any catastrophe from occurring. The safety and availability of the power plant relies on the human operators both through their ability and reliability to ensure smooth and trouble-free plant operations. Training the operators on normal plant operation, maintenance, fault diagnosis and unforeseen emergencies in the plant helps reduce the latency period of the plant and thus increase the efficiency. Operator Training Simulator has become an indispensable entity in imparting hands on training to these operators. Development of process simulators calls for the process to be designed, modeled and implemented to replicate the real plant in steady state and transient conditions.

The Proportional Integral Derivative (PID) controller is the most widely used control strategy in the Industry. The popularity of PID controllers can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. Biologically inspired evolutionary strategies have gained importance over other strategies because of their consistent performance over wide range of process models and their flexibility. The level control systems on Deaerator, Feed Water Heaters, and Condenser Hot well are critical to the proper operation of the units in Nuclear Power plants. For Precise control of level, available tuning technologies based on conventional optimization methods are found to be inadequate as these conventional methods are having limitations. To overcome the limitations, alternate tuning techniques based on Genetic Algorithm are emerging.
This paper analyses the manual tuning techniques and compares the same with Genetic Algorithm tuning methods for tuning PID controllers for level control system and testing of the quality of process control in the simulation environment of PFBR Operator Training Simulator(OTS).