Assessment of maximum loadability limit using evolutionary algorithms and its improvement using facts devices

Abstract

Infrastructure innovation in the power system industry encourages newlinemore partakers to take part in the electricity market which improves the load utilization level So the maintenance of the power system s agility with respect to any dynamic update in terms of load level is necessary Precise prediction of maximum allowable loading point helps to enhance the power system agility and also improves the total allowable power transfer capability which in turn helps to supply continuous eminent power supply at the minimal cost to the customers by means of encouraging more contracts Considering the above potential newlinebenefits in this thesis by using individual incremental loading factor IILF the precise prediction of total loadability limit TLL of the system is manipulated with the help of newly evolved meta heuristic optimization algorithms such as Grey Wolf optimizer GWO Flower Pollination Algorithm FPA and Artificial Bee Colony Algorithm ABC The allowable single line contingency scenario is considered along with base case scenario to extract more realistic TLL which newlinehelps to maintain the power system balance with respect to the dynamic nature of the load The proposed maximum loading point extraction manipulation solution problem was tested with the help of three standard IEEE test systems 30 Bus 57 Bus and 118 bus systems and practical south Indian grid 21 bus systems The extracted test results show that the predicted maximum allowable loading point enhances the load utilization level without affecting the system securities The statistical performance measures of GWO FPA and ABC confirmed the better balance of exploration and exploitation in extracting the optimal results newlineAmong the three algorithms GWO gave better results newline newline