Considering environmental concerns regarding air pollution which is induced by burning fossil fuels to generate electrical power, utilizing solar energy as a green and sustainable energy source is of great interest. This study proposes a novel framework to determine the optimal location for constructing solar photovoltaic (PV) farms. To locate the suitable areas for PV farms, firstly, a fuzzy-based method is utilized to homogenize the input parameters, thereafter, the analytical hierarchy process (AHP) and Dempster-Shafer (DS) methods are independently used. In the AHP method, the proper weight for each input parameter is generated utilizing a pairwise comparison matrix. However, the DS method identifies output in different confident levels. Finally, southeast of Fars province in Iran as a region with high sunny hours in the year is selected, and the applicability of proposed methods is examined. The results show that 32% of the case study is located at high and good suitability classes in the fuzzy_AHP method. However, it is 18.56%, 16.70%, 16.32% according to 95%, 99% and 99.5% confident levels in the fuzzy_DS method, respectively. Comparisons of the fuzzy_AHP and fuzzy_DS methods at 20 points with various solar radiation intensities and the number of dusty days parameters indicate that the fuzzy_DS method can more reliably determine the optimal PV farm locations. Additionally, as the fuzzy_DS method determines the optimal locations with different confident levels, this method can benefit decision-makers to determine the risks associated with selecting a specific site for constructing solar PV farms.

Brushless machines with interior permanent magnets (PMs) are mostly made with PM geometries inconsistent with the polar coordinates of the rotor cross-section. Therefore, without any sub-domain transformation, the analytical approaches cannot precisely model the interior PM machines in a single coordinate. This paper presents an accurate two-dimensional (2-D) analytical model for electromagnetic analysis of slotted brushless machines with rectangular spoke permanent magnets (PMs) assisted by hub magnets. The slotting effect is modeled via sub-domain technique. However, the spoke PM is not consistent enough with polar coordinates to be approximated by one sub-domain. In order to map the spoke PMs in polar coordinates, herein, the rectangular geometry is approximated by a combination of arc shaped sub-domains. On this basis, the 2-D analytic model is developed for M-fraction spoke PM. To verify the accuracy of the proposed 2-D approach, analytic results are compared with numeric results of finite elements method (FEM). It is revealed that by dividing the spoke PM into arc sub-domains, the 2-D model achieves higher accuracy in calculation of electromagnetic quantities such as PM and armature reaction (AR) fields, electromagnetic torque and Back Electromotive Force (BEMF).

In this paper, a hybrid approach is proposed to detect the broken rotor bar and rotor mixed eccentricity faults of three-phase squirrel cage induction motors based on one phase of the stator current signal. The hybrid method is based on a combination of discrete wavelet transform (DWT), Teager–Kaiser energy operator (TKEO) and power spectral density (PSD). At the first stage, DWT is applied on the stator current signal; then TKEO is applied on some of the DWT detail coefficients and finally PSD is used to extract the required feature to detect the aforementioned faults. The proposed method is tested on an experimental test-rig having a 1.5 kW induction motor under various load conditions and three different power supplies: 1) directly supplied by power grid; 2) supplied by a drive system based on scalar control technique; and 3) supplied by a drive system based on direct torque control (DTC) technique. The results show the efficacy of the proposed method in accurate detection of the broken rotor bar and rotor mixed eccentricity faults.

The fuel consumption and pollutant emissions of vehicles can significantly be reduced by adding an electric drivetrain to the internal combustion engine (ICE) without deteriorating the vehicle performance. One of the best suggestions to achieve such a goal is Plug-in Hybrid Electric Vehicles (PHEVs). In PHEVs, the design of an efficient energy management strategy (EMS) plays a significant role. In this paper, a new control strategy is proposed for parallel PHEV to keep the ICE operation points close to the high efficiency region while minimizing the fuel consumption over the standardized drive cycles. First, a new high efficiency ICE on/off control strategy is designed. Second, the teaching-learning based optimization (TLBO) algorithm is used to optimize the proposed control strategy and find the optimal ICE operation region. In the rule-based controllers, the ICE operates within predefined constant efficiency region without incorporating the driving pattern. However, the proposed controller can optimally tune the ICE operation points based on each driving scenario to achieve a better fuel economy. Since the control strategy of PHEVs highly depends on the driving patterns, three different drive cycles with three different trip distances and one recharging opportunity during the trip are considered to show the effectiveness of the proposed technique in terms of the fuel economy and the ICE efficiency. The results illustrate that the proposed control strategy could greatly decrease the fuel consumption of the PHEV under all driving scenarios.

This paper presents a two-dimensional (2-D) analytical model for plannar linear permanent-magnet (PM) synchronous motors. The general and particular solutions are presented for a slotless single-sided linear PM synchronous motor as a case study. The governing partial differential equations (PDEs) of the motor are formulated for seven regions: mover-side exterior, mover back iron, PMs, air-gap, winding, stator back-iron and stator-side exterior. Four different magnetization patterns, i.e. parallel, ideal Halbach, 2-segment Halbach and bar magnets in shifting directions magnetization patterns are considered to calculate the tangential and normal components of the open-circuit magnetic flux density, and armature reaction of the motor under the study. The winding induced voltage, flux linkage, self and mutual inductances, normal and tangential components of forces are computed. The validity of the proposed technique is confirmed by comparing the analytical results with the results obtained from 2-D finite-element analysis.

This article presents a method based on vibration signals to diagnose rotor mixed eccentricity faults in three-phase squirrel cage induction motors (SCIMs). Rotor eccentricity is a typical fault in SCIMs that significantly affects the performance of electrical machines and results in unwanted vibrations. The time synchronous averaging (TSA) signal, which consists of the harmonics components generated by the fault, is obtained via the characteristic frequency of the eccentricity fault. TSA is decomposed into two signals: TSA-regular and TSA-difference, which are respectively similar to approximate and details of the wavelet transform. Based on the fault index, which is the maximum FFT value of the TSA-difference signal, the healthy and faulty motors can be distinguished. The proposed approach is evaluated on an experimental test rig equipped with a 1.5 kW SCIM supplied directly from the power grid under various load conditions. The results illustrate the effectiveness of the proposed approach in detecting the rotor mixed eccentricity faults.