DEVELOPMENT OF A POWER MANAGEMENT ALGORITHM FOR ELECTRICAL POWER NETWORK

This project work is aimed at developing an efficient Algorithm for the management of Electric Power network using fuzzy logic. The fuzzy logic model functions as a system operator in making decision for load shedding and transfer switching. The new technique uses the system data frequency variation, load variation and voltage variation and the experience of the system operators to formulate fuzzy rules, which are then simulated using fuzzy logic toolbox in MATLAB.

The fuzzy controller for the load shedding management of power system, was modeled and developed. Data collected from the New Haven Electric Power Distribution Substation was used to formulate the fuzzy logic interference rules. Simulation results indicates a remarkable improvement in the performance of the load shedding management at the power plants. Using the fuzzy controller the delay in load shedding transfer switching was reduced from 600 s to 0.02316 s, representing 99.99% reduction in load shedding transfer switching. The fuzzy logic controller achieved a power management efficiency of 90.57%.

Future development and present operation of electric power system along

with other large system must pursue a number of different goals. Above all, the power system should be economically efficient, if it should provide reliable energy supply and should not have any detrimental impact on the environment. In addition to these global goals there is a number of supplementary goals, objectives and criteria. At the same time, operation and development of the system network is influenced by a variety of uncertain and random factors. As a result, the development strategy can be chosen from a large number of possible alternatives. Obviously, among a set of possible alternatives the developer attempts to find the best, or in accordance with accepted term, the optimal alternative. Thus, the complexity of the problem related to power systems planning is mainly caused by presence of multiple objectives, uncertain information and large number of variables.