فصلنامه روشهای هوشمند در صنعت برق
پیاپی 43 (پاییز 1399)

In phased array systems, increasing the array length and consequently increasing the number of antenna elements are needed to improve spatial resolution. It can be addressed by MIMO radars using orthogonal signals from multiple transmitters and using the virtual array. Virtual element's locations are derived from the convolution of the physical elements of the transmitter and receiver. To understand the functionality of MIMO radars, virtual array has been introduced. As the virtual array be larger, the radar angle resolution increase. Increasing the degree of freedom of the virtual array is highly dependent on the array of transmitter and receiver elements placement. The Nested Array which is one of the well-known methods in alignment of MIMO radar array elements, is investigated. In this article, the new nested array is examined and implemented in one dimension. The very innovative idea of this article is to use the new 2D nested array. A multi-step approach for implementing two-dimensional arrays using a new nested array for a MIMO radar is presented which can be generalized to any other structure with different number of elements. Finally, the advantages of the proposed method in terms of angular bandwidth of the main radar are presented. The results show that the proposed method is much more efficient than the other methods. The comparative results presented at the end of this article shows that the half-power beam width of the proposed method is approximately 0.3 degrees while this value for the nested method is more than 2 degrees.

The control of artificial limbs can be done by distinguishing the patterns of imagined movement using the EEG signals. The aim of this study was to identify hand and foot imagery movements based on EEG signals. The Iva dataset of BCI Competition III, which includes EEG signals from 5 healthy individuals in C3, C4 and CZ channels, was used to design the imagery movements detection system. Initially, the basic components of EEG signal noise were removed using the MSPCA method. In the next step, the EEG signals were decomposed in two different ways including frequency filtering using the Butterworth filter and the wavelet packet transform (WPT). In this study, the detrended Fluctuation Analysis, Fractal dimension, Correlation dimension, Lempel-ziv complexity and Entropy as nonlinear dynamics features, were calculated for the signals. In both decomposition methods, the desired features were calculated for the temporal version of the specified subbands. In order to determine the best performance of the system, different combinations of the channels and the features were evaluated. The wavelet-based decomposition method, in the case of using all three channels and five features, provided the highest recognition accuracy; So that using support vector machine (SVM) classification method, the accuracy of 93% was obtained in identifying the desired movements.

Nowadays, the wireless sensor network (WSN) is used in many different fields and applications. Enemy tracking and wildlife habitats monitoring are the examples of target tracking by using of large number of sensor nodes. The main idea in this area is to find some usable target information such as location, speed and movement direction of the target because they must be available any time. By the way, the sensor nodes in sensor network model have a severe energy limit and cannot be recharged simply. In this paper, an efficient algorithm abbreviated EAASA is presented in order to reduce energy consumption while maintaining the quality of target tracking. The simulation results are compared to the AASA (cluster-based target tracking algorithm) algorithm and show that the proposed algorithm has been able to reduce energy consumption significantly while maintaining tracking quality. This method has increased the life time of the network and reduced the rate of loss of the target.

By incremental deployment of renewable energy sources on microgrid frameworks, new technical and economic issues have emerged in the power system industry. The optimal operation of microgrids in the presence of intermittent renewable sources has been counted as a new challenge within the last decade. Microgrids are off-grid or grid-connected power systems on a very small scale encompassing different types of distributed generation sources and local loads. Generally, in isolated microgrids, the demanded energy of consumers is maintained by hybrid models of internal energy sources. The principal purpose of hybrid systems is to supply the electrical power demanded by consumers instantaneously as well as storing surplus energy for critical conditions. In this paper, a techno-economic and environmental base approach for optimal energy management of microgrids using crow search algorithm is presented. Under study microgrid include renewable energy resources, battery and diesel generator as backup power generator. Annual cost and the released emission are considered as the objective function of the proposed method. The crow search algorithm calculates power dispatch scheduling among generation units. Simulation results of the proposed method show the appropriate configuration of the hybrid system that lead to decrease the annual cost of the system and the released emission.

Vehicle ad-hoc networks are examples of mobile ad-hoc networks where vehicles are the mobile nodes. Inthese networks, vehicles are interconnected and can send messages to each other, but also to the roadsideinfrastructure. In such networks, routing is essential for network design. Poor design causes seriousproblems for vehicle networks. Multi-hop data transmission over VANET networks is a complex task,since network nodes are very mobile and therefore very likely to be disconnected. Therefore, informationdissemination techniques in car networks are very important and have received special attention in recent years. In this research, we present a distributed routing protocol with end-to-end delay computation approach to the generated paths before sending the data packet. This protocol creates a stable backbone on the road components and connects them through bridge nodes. Bridge nodes allocate weight to road components, which it does on the basis of information gathered from delays in routes and communication quality. The simulation results show the success of the proposed protocol compared to two well-known vehicle network protocols including AODV and AOMDV.

Optimal placement and sizing of equipment in engineering systems is one of the most important and challenging practical problems. Installation of capacitors and distributed generations (DGs) in distribution systems has many benefits, such as improving the reliability as well as reducing the power loss, however, includes high investment costs. Hence, the maximum use of these benefits highly depends on choosing suitable locations for installation. In this paper, the Success Rate Group Search Algorithm (SRGSO) is used for optimal placement and sizing of DG sources and capacitor banks simultaneously in distributed systems. The used objective function includes costs of Expected interruption (ECOST), Energy Not Supplied (ENS), active power losses and DG and capacitor investment, operation and maintenance. To evaluate the effectiveness of the proposed algorithm in finding the optimal solution, various capacitor and DG placement scenarios are simulated on the IEEE 33-bus radial distribution test system. The simulation results show that the proposed method is more effective and has higher capability for solving the problem of capacitor and DG placement, compared to previously proposed methods. Using the proposed method in this paper, the value of active power losses as well as bus voltage profile are improved more than other investigated methods.