water cycle algorithm(wca)

Estimating the water inflows to water resources systems is one of the essential measures for awareness of planning and allocating optimal water resources in different sectors of consumption. In this study, a combination of Meta Heuristic algorithms including the Water Cycle Algorithms (WCA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Neural Network Algorithm (NNA) and Grasshopper Optimization Algorithm (GOA) for Neural-Fuzzy System training and update parameters was used. Finally, the best models were developed to predict the daily inflow of Sefidrood reservoir dam. This method does not suffer from the problems of training gradient-based algorithms. Four features including dam lake area, reservoir volume and reservoir level of the dam in the 7 lags and inlet flow in the 1 lag were selected according to the AutocVarious statistical indicators were used to evaluate the performance of utilized models. In the test stage, ANFIS-WCA model presents the lowest values of SI, MAE and NRMSE equal to 0.0736, 0.05048 and 0.0736, respectively, and the maximum value of R^2equals to 0.9840, which indicates its superiority over other models. Based on GPI index, ANFIS-WCA model was selected as the best model and then ANFIS-NNA, ANFIS-GOA and ANFIS-WOA models were ranked, while the worst accuracy was obtained through the ANFIS-GOA model. The high accuracy of the ANFIS-WCA model compared to other hybrid models indicates the performance of the WCA for escaping local optimization in combination with the ANFIS model which has enabled this algorithm as a powerful tool for estimating the inflow of Sefidrood dam.

Unified Power Flow Controller (UPFC) is one of the FACTS devices which plays a crucial role in simultaneous regulating active and reactive power, improving system load, reducing congestion and cost of production. Therefore, determining the optimum location of such equipment in order to improve the performance of the network is significant. In this paper, WCA algorithm is used to locate the optimal placement of UPFC. The concepts and ideas underlying this approach are inspired by nature and are based on observations of water and the process of the water cycle and the formation of rivers and streams and their flow to the sea in the real world. This algorithm has a high potential for escaping local optimizations as well as speeding up global optimization. For UPFC modeling, the power injection model has been used and the OPF-UPFC problem has been solved with various objective functions such as generators fuel cost, network losses and system loading, both single-objective and multi-objective. Also, the results obtained from different objective functions are compared with other references. The results acquired show the efficiency and speed of the water cycle algorithm compared to some other intelligent algorithms. The computer program has been written in the MATLAB software environment and implemented on the IEEE 14-bus network.