Abstract:
Existing power system control and automation is based on supervisory control and data acquisition model. A central master station located at the control focal point gathers information from a number of remote units. While this control methodology provides acceptable performance and reliability, it has a number of drawbacks particularly in the areas of flexibility and access to information. Control of complex, diverse, distributed and interconnected power networks demands devising techniques capable of intervening locally to fix and save the problem from propagation through the network.
An innovation conceptual model based upon distributed system with decision making relegated to individual’s location instead of centralized control has been proposed. Agents have been conceived and assigned various important tasks with autonomous functioning. Object model of the multiagent system comprises of attributes and services expected to be performed by each agent. It is followed by a use-case model. The use-case model is based upon miltiagent system of the identified tasks and specifies handled data by each use-case. It also includes predetermined stimuli and expected responses by use-cases on receipt of stimulus. Detailed state machine model is proposed showing sequences and interactions of different states of agents. Standard UML notations have been used. Finally a power has been simulated in Matlab/ Simulink and results of timely response for retaining synchronism by distributed control as against limitations of central control have been included. Results indicate better capability of distributed control with regard to making autonomous decisions to save from going out of synchronism or from overloading.
At the end of thesis, a phased approach for development in various fields of technologies has been given for actual implementation of most modern mechanism for control and functioning of complex distributed power networks as proposed here. The suggested model provides a comprehensive solution to problems envisaged due to increasing complexity and diversity of these networks by making local decisions, instead of relying on a central control station.
