Many noncoherent distributed strategies for cooperative sensor networks that do not require channel knowledge at any antenna to overcome the overhead involved in channel estimation are lately suggested; however, these strategies suffer from low system performance in terms of bit error rate (BER) and a comparably high decoding complexity. Differential beamforming strategies have recently been proposed to overcome these problems; however, they are implemented using the four-phase protocol. Thus, we propose a new strategy based on the three-phase protocol to increase the symbol rate. By doing this, a significant improvement can be achieved in the overall system performance. Hence, in this article, a new bidirectional differential beamforming strategy is suggested: 1) to be applied on the three-phase protocol instead of the four-phase protocol; 2) to be applicable for a decentralized wireless sensor network using single-antenna sensors distributed randomly between the communicating base stations; 3) to enjoy low decoding complexity; and 4) to improve the network performance in terms of BER by maximizing the received signal-to-noise ratio at the receiving base station without requiring channel knowledge at any antenna in the whole network. From our simulation results, the proposed strategy shows a substantially improved BER performance compared with the current state-of-the-art ones.

Based on the optimized coprime array (OpCA), we propose a real-valued Toeplitz matrix method. This article analyzes the feasibility of converting the manifold matrix of a virtual domain constructed from a physical array into a real-valued matrix, and the unitary estimation of signal parameters via rotational invariance technique is introduced to transform the equivalent received signal rearranged Toeplitz matrix into a real-valued matrix and obtain directions of arrival (DOAs). Moreover, the proposed method offers higher estimation accuracy. Simulations are demonstrated to prove the validity of this method.

This paper introduces an obstacle-aware fuzzy-based scheme for the localization of wireless chargers in wireless sensor networks (WSNs), which aims to deploy chargers in both fixed and/or multiple duty-cycled networks. A power harvesting model is proposed that considers the obstacle penetration loss in the harvested power from multiple chargers. We take advantage of the fuzzy control system (FCS) scheme to gain prior knowledge about the distribution pattern of the nodes. The simulation results confirm that deploying chargers based on this scheme decreases the required number of chargers, in comparison with the nonfuzzy algorithms by up to 59.6% in multiduty-cycled and 34.4% in fixed duty-cycled scenarios, in the best cases.

In induction motors, the leakage reactance values are obtained from the motor short circuit operation experiment. However, these values do not give clear results for different geometries. For this reason, taking into the parameters of the slot geometry accounts for determining the stator and rotor leakage reactance values that are important in terms of obtaining more accurate results. This paper has investigated the stator slot permeance values of nine different stator slot structures commonly used in industrial type of three-phase squirrel cage induction motors for obtaining the best starting torque. The stator inductance and leakage reactance values were calculated for each stator slot structure. The change of starting torque of the induction motor has been analyzed by using the MATLAB code. As a result of the analysis, the stator slot structure, which gives the best starting torque, has been obtained. It has been also shown that a 16% improvement can be achieved by changing the stator slot structure at the starting torque. This increase in the starting torque will provide a great advantage for the motor's acceleration versus a heavy load.

There is a growing tendency for developing real-time clustering of continuous stream data. In this regard, a few attempts have been made to improve the off-line phase of stream clustering methods, whereas these methods almost use a simple distance function in their online phase. In practice, clusters have complex shapes, and therefore, measuring the distance of incoming samples to the mean of asymmetric microclusters might mislead incoming samples to irrelevant microclusters. In this paper, a novel framework is proposed, which can enhance the online phase of all stream clustering methods. In this manner, for each microcluster for which its population exceeds a threshold, a classifier is exclusively trained to capture its boundary and statistical properties. Thus, incoming samples are assigned to the microclusters according to the classifiers⣙ scores. Here, the incremental NaAˉve Bayes classifier is chosen, due to its fast learning property. DenStream and CluStream as the state-of-the-art methods were chosen and their performance was assessed over nine synthetic and real data sets, with and without applying the proposed framework. The comparative results in terms of purity, general recall, general precision, concept change traceability, computational complexity, and robustness against noise over the data sets imply the superiority of the modified methods to their original versions.

Electrical submersible pump (ESP) systems in the oil field have unique features due to the long cabling effect. The electrical system for a typical ESP installation consists of a variable frequency drive (VFD), a multi-tap step-up transformer, a surface cable, a downhole main cable, a motor lead extension (MLE) cable, and an electrical submersible motor. A load filter is usually required in a VFD-ESP system. In this paper, several operational parameters that can significantly affect operating conditions of an ESP system are demonstrated through field measurements and simulation of a practical ESP system currently in operation in Alberta, Canada. The simulation model is also validated by comparing with field measurement data. This analysis is beneficial to field engineers and ESP system operators to understand better the impacts of equipment parameter settings.

As the ultrahigh-voltage (UHV) projects are rapidly developing, it is necessary to investigate the problems involved in overvoltage and insulation, thereby providing a technical basis for future UHV projects. The switching overvoltage of closing no-load lines was simulated and analyzed here using two kinds of typical UHV network structures: the power plant to the grid system and the grid-to-grid system. Different factors, such as line length, mode of operation on the power plant side, and short-circuit capacity, are considered. It is possible for lines shorter than 60 km to eliminate the closing resistor, determined by calculating the probability of flashover. We analyzed the phase selection closing in depth to inhibit the switching overvoltage and considered the factors such as phase deviation and the length of the line. Finally, the applicability of phase selection closing is put forward for lines of different lengths.

In this paper, the three-phase inverter power efficiency of a brushless DC (BLDC) motor drive is analyzed theoretically and verified experimentally. An inverted pulsewidth modulation driving scheme has higher power efficiency than a conventional six-step driving scheme, particularly under low rotor speed due to less diode conduction power loss of Sync metal–oxide–semiconductor field-effect transistors (MOSFETs). However, the difference in the power efficiency decreases as the rotor speed increases; for a rotor speed above 1000 r/min, the difference in the power efficiency is negligible. In addition, the power efficiency of the inverted driving scheme drops further than one for the conventional six-step driving scheme with sampling frequency increase. It is due to the additional switching power loss of Sync MOSFET. The theoretical analysis of power loss in a three-phase inverter verifies the experimental results.

Accurate load forecasting is a critical step for power system generation planning. Contingency parameters of the system and their dynamic characteristics should be taken into account for the purpose of load forecasting. In this paper, a probabilistic load forecasting algorithm considering contingency parameters is developed for the peak load forecasting. Using the chi-square distribution test and historical data, the probabilistic distribution of contingency parameters can be determined. In a case study, the Monte Carlo simulation is run to forecast load demand and generation scenarios of Bangladesh based on the developed adaptive algorithm and the calculated probabilistic distribution. The influence of contingency parameters is evaluated using a Bayesian network in a sensitivity study.