Massive multi-input multi-output (MIMO) has attracted significant interest in academia and industry, which can efficiently increase the transmission rate. However, the error rate of conventional channel equalizations in massive MIMO systems may be high owing to the dynamic channel states in practical conditions. To solve this problem, in this paper, we propose an improved channel equalization framework based on the deep neural network (DNN). Based on the analyzed relationship between the input and output of the DNN, the data can be recovered without the channel state information. Furthermore, aiming at reducing the convergence time and enhancing the learning ability of the DNN, a classification weighted algorithm is proposed to optimize the cost function of the DNN, which is named as classification weighted deep neural network (CW-DNN). Simulation results demonstrate that compared to conventional counterparts, the proposed CW-DNN based equalizer can achieve a better normalized mean squ
This paper proposes a novel particle swarm optimization (PSO) algorithm based fuzzy logic controller (FLC) for improving the performance of automatic solid-state transfer switch (SSTS) with respect to transfer time. The proposed technique generates adaptive membership functions (MFs) of voltage error and rate of change of voltage error for input and output based on the fitness function formulated by the PSO. An optimal PSO-based FLC (PSOF) fitness function is further employed to tune and minimize the mean absolute error (MAE) to improve the performance of the load transfer in a short duration. Results obtained from the proposed PSOF are compared with those obtained with the conventional FLC to validate the developed controller. It is observed that the proposed PSOF optimized controller can transfer the load faster than the conventional FLC controller. The accuracy of the developed PSOF is illustrated and investigated via simulation tests for SSTS in the IEEE 9-bus system. It can be con
Distributed energy resource (DER) in microgrid has emerged significant challenges in the existing centralized energy management systems. This is due to the stochastic energy sources integrated into microgrid and dynamic power demand that has brought difficulties in controlling the optimal output power. An inefficient and without optimally controlled DERs and charge/discharge of energy storage system results in high operating cost to consumers as well as decrease a lifetime of energy storage based microgrid. Therefore, to solve the issues, a day-ahead optimized scheduling controller-based novel lightning search algorithm (LSA) technique is introduced to provide an optimum power delivery with minimum cost including optimum use of energy storage. The main objective of the proposed controller is to develop an optimized controller for the microgrid to minimize the operating cost of DER and optimal operation of charge/discharge of the energy storage system. The optimized controller's ef