The three-phase pulse-width modulating (PWM) voltage source rectifier (VSR) system is widely used because of its advantages in performance. Numerous studies show that power semiconductors are significant contributors to the overall failure rate of power converters. This study presents an online diagnosis method for a single open-switch fault of a three-phase PWM VSR, because existing research mainly focuses on fault diagnosis of inverters. The proposed method employs current distortion to diagnose faults in a convenient and simple way. It requires no extra hardware but only AC current signals, which are available in the control system and can be embedded into the existing driving software as a subroutine without excessive computational effort. The proposed method offers high diagnostic effectiveness and reliability. Experimental results show that the proposed method can diagnose the faulty switch at low currents as well as under abrupt load transient conditions without false alarms. Furthermore, the average diagnostic time of this method is only 1.8 ms.

In modern monitoring systems, it is essential to deploy sensor nodes and deliver related data to the information center. Wireless sensor networks (WSNs) usually work in harsh environments with vibration, temperature variations, noise, humidity, and so on. The batteries of sensor nodes are always not replaceable because of difficult access. Most of existing literature tries to prolong network lifetime by improving sleep scheduling strategies and deployment methods, independently or jointly. However, the congenital defects of mesh network can't be avoided completely. To overcome the technology challenges, this paper develops a LoRaWAN-based WSN and investigates its energy efficient scheduling method. Firstly, the basics and the limits of LoRaWAN are introduced and the feasibility and the considerations of LoRaWAN-based star wireless sensor network are discussed. Secondly, an improved compressed sensing algorithm named ISL0 (improved SL0) is proposed for network data reconstruction and compressed sensing algorithm can reduce the number of LoRa nodes transmitting data packets to avoid collision and latency. Thirdly, a sleep schedule method is proposed to reliably monitor environment data and device operating status. By using the proposed method, not only the abnormal information can be detected on time, but also the overall network data can be recorded termly. Simulation and measurement results verify all nodes have same power level at different times, and the network lifetime is maximized. INDEX TERMS WSNs, LoRa, LoRaWAN, energy efficient scheduling, compressed sensing.

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Compressed sensing (CS) based channel estimation methods can effectively acquire channel state information for Massive MIMO wireless powered communication networks. In order to solve the problem that the existing sparsity-based adaptive matching pursuit (SAMP) channel estimation algorithm is unstable under low signal to noise ratio (SNR), an optimized adaptive matching pursuit (OAMP) algorithm is proposed in this paper. First, the channel is pre-estimated. Next, the energy entropy-based order determination is raised to optimize the reconstruction performance of the algorithm. Then, a staged adaptive variable step size method is put forward to further promote the accuracy of channel estimation. Finally, theoretical analysis and simulation results demonstrate that the proposed OAMP algorithm improves the accuracy at the expense of a small amount of time complexity, does not require a priori knowledge of sparsity and its comprehensive performance is superior to other existing channel estimation algorithms.INDEX TERMS Massive MIMO, wireless powered communication networks, sparse channel estimation, sparsity-based adaptive matching pursuit, energy entropy-based order determination, staged adaptive variable step size.