PLC Implementation of Supervisory Control for a Dynamic Power Flow Controller using a Modular Approach

Dynamic Power Flow Controller (DPFC) provides steady-state and dynamic power flow control for power lines and is considered as a Flexible AC Transmission System (FACTS) controller. This paper deals with control of a standard DPFC using a Discrete Event System model. The Supervisory Control of DES has been used to implement Modular supervisors for the DPFC. Despite the fact that the SCT is well consolidated, with a large number of publications focusing on the theoretical aspects, the industrial application is unknown. It is mainly due to the complexity of the theory. The numbers of states and events to be controlled are very large even for the seemingly simple systems. In recent years, a model for modular approach to the Supervisory Control for performing the formal synthesis of Supervisors has been proposed. Programmable Logic Controllers are used for the physical implementation of the controllers. Some problems in physical realization of Supervisors in PLCs are dealt with

A Real Time Model For Prediction Of Blast Furnace Hot Temperature Through Neural Network

The thermodynamic processes inside the Blast Furnace are very complex and it’s ex difficult to analyze the ongoing process through physical measurements. The same can be estimated by monitoring measurable parameters which represent and indicate the state of the furnace. The hot metal and slag temperature, hot metal and slag chemical a blast temperature and volume are some of the parameters which have a bearing on the state of the furnace. Hot metal temperature is an important factor that not only depicts of the furnace but also determines the quality of pig iron. Hence, aiming at maintenconsistent the hot metaltemperature to achieve a stable operation of the furnace iso importance. Neural networks are parallel machines that have the ability to build and define relationships between various parameters and are self-learning to dynamically respon variations in operating conditions. The neural networks are capable of generating linear as nonlinear relationship between the parameters. This paper describes the approach adestimate the blast furnace parameters denoting the internal conditions which can ser guide line to the operator to take corrective actions in order to maintain smooth operatiofurnace and consistent production. The model developed has been tested and validated o periods with online data from furnace instrumentation to build the reliability of the preThe model is then integrated with the blast furnace automation to predict the Hotemperature in real time. The accuracy of prediction achieved is more than 90 %