Reducing the unbalanced magnetic forces (UMFs) and torque ripple (TR) simultaneously is one of the most vital goals of electrical machines designed for a variety of applications, such as hybrid vehicles. Removing TR is causing vibration free performance as well as UMFs decreasing will increase the life of machine components. Although several quantities impact the mentioned indicators, the first one is type of magnetization. Hence, two conventional magnetization patterns including radial and 9-segment magnetization patterns are thought through. Furthermore, pole arc to pole pitch ratio is extremely influential. Therefore, two functions based on it are determined for UMF and TR. Several magnetization patterns are considered to provide suitable response. They are optimized by multiobjective meta-heuristic optimization algorithms. Three algorithms including Pareto envelope-based selection algorithm II, nondominate sorting genetic algorithm II, and multiobjective particle swarm optimization have been utilized because their performances depend on not only initial guess but also type of problem. Then, the optimization results have been compared. Next, the best machine's dimensions are selected. After that, cogging, reluctance and instantaneous torque, overload capability curve and torque-speed characteristic have been computed. Finally, the temperature impact on either UMF's and torque's average or mentioned indicators is analyzed. It should be noted that the function fitting and optimization process have been done using MATLAB software.

A discernible rise in the frequency and intensity of unpredictable events and their significant social and economic effects have made power system planners pay special attention to the security and reliability of power networks. For this purpose, this article tries to provide an efficient model for strengthening the security of active distribution networks based on multiple microgrids by optimally using energy storage resources and consumption management plans. In the proposed plan, a hierarchical two-stage approach has been developed in which the first stage models the incident and their impact on the distribution network. Then, in the next stage, preventive and corrective measures are implemented to increase system readiness and reduce damages caused by severe accidents. At this stage, various tools such as energy storage, distributed and renewable production sources have been used in addition to responsive loads. In the corrective phase, the independent microgrid partitioning method is used to restore the distribution network to increase the speed of load pickup process. In order to consider the uncertainty and the risk caused by events on the performance of the proposed plan, the problem is done by robust optimization to obtain more realistic results. Finally, in order to confirm the effectiveness of the proposed method in improving the security of distribution systems, a standard network of 33 buses was used with different operating conditions. The results confirm that the proposed security-constrained robust optimization plan not only reduces system operating costs, but also improves consumer security in the face of severe accidents. Although the increase in network security is associated with a slight increase in system costs, the benefit of reducing outages and optimal use of network capacity has led to the effectiveness of the proposed plan.

In this paper, a permanent magnet synchronous machine (PMSM) with an auxiliary winding (AW) on the rotor is analyzed by two-dimensional approach. This PMSM with AW (AWPMSM) can be used in many applications such as propulsion system, aircraft and traction because it includes rotor flux control capability. First, the magnetic field in different parts of AWPMSM is calculated based on Maxwell equations. Then, as a consequence of the magnetic field, the torque components, including cogging, reluctance, electromagnetic and instantaneous torque are computed. Next, torque-speed characteristic has been investigated. This AWPMSM can be located in the flux weakening mode in two ways, first one is to attenuate the rotor field by changing the direction of the AW field and the other one is to adjust the armature current angle, both of them have been investigated. After that, the overload capability and temperature effects have been analyzed. Finally, using the meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, differential evolution and teaching learn base optimization the dimensions of AWPMSM and the initial angle of the rotor are determined in such a way that the torque-to-volume ratio is maximized. The influences of the type of armature winding and the magnetization patterns have also been investigated. The results obtained by the two-dimensional method have been confirmed numerically.

This paper presents a new method of localizing radio frequency (RF) source in non-line of sight (NLOS) using data collected using the anchor and map. The measurable observation in the unmanned aerial vehicle (UAV) is assumed to be the received signal strength indicator (RSSI), and a method is presented based on the RSSI observation of the reflected signal sent from the anchor to estimate the location of the reflecting obstacle, which is a two-step method for map estimation and localization. It is also assumed that the map of the obstacle location is also available; the location of the reflective obstacle can be obtained using the map with an error. And finally, by combining this data in a weighted and improved particle filter for the optimal use of the number of particles in a wide area, the location of the unknown RF source is estimated more accurately. It was revealed that the proposed method improved localization and had good precision.

In this paper, RF source localization in non-line of sight condition, using map of the obstacles is proposed. Received signal strength indicator (RSSI) and angle of arrival (AOA) measurements are observations which are obtained from received signal on the UAV. In the proposed approach, AOA are used to determine the obstacle on the map from which the reflection has happened. Then the RSSI information is used to determine the location of the RF source. In the basic version of approach, triangulation is used to determine the location of the RF source. In the advanced approach, the reflection angle is also estimated to improve the localization accuracy. The estimation is done using particle filter approach. In addition, it is shown analytically that the maximum localization error for the advanced approach is bounded but relative formation of the reflectors with respect to each other can increase the localization error for the basic approach.

In this paper, an RF source localization approach, in non-line of sight condition, using reflected signals, is introduced in which received signal strength indicator and angle of arrival (AOA) observations are used. Furthermore, the optimal positions of the receiver to increase the accuracy of the localization are discussed. In the proposed approach, a reflected signal is modelled by three parameters, i.e., distance between the RF source and the receiver via the reflector, the reflection angle, and the AOA. These parameters are used to determine a locus for the RF source location. Then, by using at least two reflected signals to give two loci for the RF source location, the location of the RF source is determined. Furthermore, the optimal locus of one reflection with respect to the other one is determined. The approach has been simulated, and the results show better accuracy of localization for optimal pairs compared to the other pairs, which decreases the search time.

This paper introduces RF source localization in the absence of Line Of Sight signal reception considering the reflection phenomena. Reflection that can reduce the signal strength due to the angle of reflection and the distance between the RF source and the receiver. The proposed approach is used to localize an RF source based on the reflection model of signals from obstacles using RSSI and DOA as measurement. To estimate the reflection angle and the distance between the RF source and the receiver, as the reflection parameters, the reflection path is modeled based on the signal strength and a data base is created based on the signal propagations. Then the Nearest Neighbor algorithm is used to estimate reflection parameters for obtaining the locus of possible location of the RF source. The localization of the RF source is calculated by intersecting two locus of possible locations which can be estimated by one or two UAVs. The approach has been implemented and simulated with good results.

In this paper a beacon-based RF source localization in NLOS condition is proposed. Beacons are used to estimate the obstacle causing the NLOS condition to improve the RF source localization accuracy. The obstacle parameters are estimated using particle filter because of the nonlinearity of the problem. For localization accuracy analysis, Cramer Rao Lower Bound (CRLB) analysis is used. The approach has been simulated and compared to the basic RF source localization in NLOS condition in which obstacles are estimated without using beacons. The comparison shows that the proposed approach has higher accuracy than the basic approach. Furthermore, the CRLB analysis of the basic approach has been compared to the proposed approach which shows lower error bound for the proposed approach compared to the basic approach.

This paper represents simultaneous estimation of an RF source location and the location and height of an obstacle between the RF source and a UAV using RSSI observations. The aim is to improve the localization of the RF source by estimating the location and height of the obstacle. The effect of the obstacle on signal strength attenuation, i.e. the diffraction loss, is the most important reason that affects the performance of distance estimation based on general or empirical path loss model. Consequently, by estimating the effect of obstacle on attenuation, it will be possible to improve the accuracy of distance estimation according to path loss models. The simultaneous estimation of radio source location and the specification of an existing obstacle are implemented using particle filter, due to the complexity and nonlinear nature of the equations related to the calculation of diffraction loss. The simulation results show an improved performance of suggested filter, compared to the basic case of not estimating the obstacle.

This paper proposes a modified version of real immune algorithm (RIA) using population management, namely, RIAPM, to simultaneous coordinated tuning of power system stabilizers (PSSs) in a power system. A conventional lead-lag structure is considered for the controllers. Since, the parameters of lead-lag controllers are continuous variables to be found and an infinite number of candidate solutions will exist, therefore a modification will be considered to RIA to reduce the search space gradually, in order to get a more detailed investigation in a smaller region. Using the RIAPM, the parameters of the damping controllers are optimized simultaneously and the interactions among the controllers are considered. The numerical results are presented on a 2-area 4-machine system to illustrate the feasibility of the proposed method. To show the effectiveness of the designed controllers, a three-phase fault is applied at a bus. The simulation study shows that the RIAPM performs better than RIA.

This paper investigates the ability of Memetic Algorithm with population management (MAPM) in designing supplementary controller of High Voltage Direct Current (HVDC) link to damp the power system oscillations. A conventional lead-lag structure is considered for the supplementary controller. The aim of the proposed control strategy is to choose the best controller parameters in such a way that the dominant eigenvalues of the closed-loop system are shifted to the left-hand side of s-plane as far as possible. Also, the Memetic Algorithm without population management (MA) and Real Genetic Algorithm (RGA) is used to design a supplementary controller for HVDC. The characteristic convergence and time simulation results show that MA and MAPM algorithms have good capability in solving the problem but MAPM gives better convergence characteristics.

This paper investigates the ability of Real Genetic Algorithm (RGA) in designing supplementary controller of High Voltage Direct Current (HVDC) link to damp the power system oscillation. A conventional lead-lag structure is considered for the supplementary controller. The aim of the proposed control strategy is to choose the best controller parameters in such a way that the dominant eigenvalues of the closed-loop system are shifted to the left-hand side of s-plane as far as possible. Also, the binary version of genetic algorithm (BGA) is used to design a supplementary controller for HVDC. The characteristic convergence and time simulation results show that both versions of GA have good capability in solving the problem but RGA gives better results.