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Abstract Accurate parameter identification plays a crucial role in realizing precise modelling, design optimization, condition monitoring, and fault diagnosis of photovoltaic systems. However, due to the nonlinear, multivariate, and multistate characteristics of PV models, it is difficult to identify perfect model parameters using traditional analytical and numerical methods. Besides, some existing methods may stick in local optimum and have slow convergence speed. To address these challenges, this paper proposes an enhanced nature‐inspired OLARO algorithm for PV parameter identification under different conditions. OLARO is improved from ARO incorporating existing opposition‐based learning, Lévy flight and roulette fitness‐distance balance to improve global search capability and avoid local optima. Firstly, a novel data smoothing measure is taken to reduce the noises of I–V curves. Then, OLARO is compared with several common algorithms on different solar cells and PV modules using robustness analysis and statistical tests. The results indicate that OLARO has better ability than others to extract parameters from PV models such as single diode, double diode, and PV module models. Moreover, the convergence performance of OLARO is more excellent than the other algorithms. Additionally, the I–V curves of two PV modules under different irradiance and temperature conditions are applied to verify the robustness of the proposed parameter extraction algorithm. Besides, OLARO is successfully applied to two real operating PV modules, and it is compared with two recent well‐known methods improved by FDB. Finally, sensitivity analysis, stability analysis, and discussion of practical challenges are provided.

In recent years, motor drive systems have garnered increasing attention due to their high efficiency and superior control performance. This is especially apparent in aerospace, marine propulsion, and electric vehicles, where high performance, efficiency, and reliability are crucial. The ability of the drive system to maintain long-term fault-tolerant control (FTC) operation after a failure is essential. The likelihood of inverter failures surpasses that of other components in the drive system, highlighting its critical importance. Long-term FTC operation ensures the system retains its fundamental functions until safe repairs or replacements can be made. The focus of developing a FTC strategy has shifted from basic FTC operations to enhancing the post-fault quality to accommodate the realities of prolonged operation post-failure. This paper primarily investigates FTC strategies for inverter failures in various motor drive systems over the past decade. These strategies are categorized into three types based on post-fault operational quality: rescue, remedy, and reestablishment. The paper discusses each typical control strategy and its research focus, the strengths and weaknesses of various algorithms, and recent advancements in FTC. Finally, this review summarizes effective FTC techniques for inverter failures in motor drive systems and suggests directions for future research.

The Central African Economic and Monetary Community (CEMAC) is at a pivotal juncture in its energy development, facing significant challenges such as increasing emissions and an unreliable energy supply that hampers economic progress. This study assesses the potential for integrating diverse renewable energy (RE) sources into the CEMAC grid, specifically focusing on river hydro, dam hydropower, onshore wind, and solar photovoltaics. Using the EnergyPlan tool, we conduct a dynamic one-year simulation to model energy dispatch on monthly and hourly scales. The modelling result indicates that achieving a 100 % electricity access in the CEMAC region by 2030 will require an annual electricity demand of 8.59 TWh. If this demand is fully met by natural gas (1143 MW), it will result in about 3.75 Mt of carbon emissions. The result shows that by implementing a mix of these renewable technologies in the proposed integrated grid system, the CEMAC region could reduce its carbon emissions by up to 48.7 % relative to using a single RE source in the grid, with annual renewable electricity production of 4.19 TWh/year. Also, by maximising the RE potential from each CEMAC region, there is 49 % RE integration in the proposed integrated grid, with the highest RE share from hydro. This study quantitatively shows that the proposed synchronized regional grid incorporating these renewable sources could enhance electricity reliability and further reduce emissions in the CEMAC region. This research also highlights the transformative potential of RE in achieving sustainable and cost-effective energy solutions for CEMAC, setting a roadmap towards a resilient energy future by 2050.

Environmental assessments are critical for ensuring the sustainable development of human civilization. The integration of artificial intelligence (AI) in these assessments has shown great promise, yet the ''black box'' nature of AI models often undermines trust due to the lack of transparency in their decision-making processes, even when these models demonstrate high accuracy. To address this challenge, we evaluated the performance of a transformer model against other AI approaches, utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators. We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments, enabling the identification of individual indicators' contributions to the model's predictions. We find that the transformer model outperforms others, achieving an accuracy of about 98% and an area under the receiver operating characteristic curve (AUC) of 0.891. Regionally, the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas, level IV in the northern region, and level V in the western region. Through explainability analysis, we identify that water hardness, total dissolved solids, and arsenic concentrations are the most influential indicators in the model. Our AI-driven environmental assessment model is accurate and explainable, offering actionable insights for targeted environmental management. Furthermore, this study advances the application of AI in environmental science by presenting a robust, explainable model that bridges the gap between machine learning and environmental governance, enhancing both understanding and trust in AI-assisted environmental assessments.

The continual use of fossil fuel technologies has negatively impacted on the environment and has caused huge health challenges globally. Despite the growth of renewable energy technologies, their efficiency issues have hindered widespread adoption. The use of nanofluids as heat transfer fluids in renewable energy technologies have further improved their overall efficiency, resulting in a more environmentally friendly performance of these systems. For automotive fuel and coolant systems, hybrid nanofluids are gaining appeal due to their remarkable ability to enhance thermal performance and accelerate heat transfer rates. Ternary-hybrid nanofluids, which combines three different types of nanoparticles in a wide range of mixing ratios, are an intriguing but mostly speculative concept. Optimizing the mixing ratio for effective heat transfer characteristics is important for energy applications. A unique Al2O3/ZnO/Fe3O4 ternary nanofluid is synthesized and its density is measured in this investigation. The nanofluid preparation included three different mixing ratios (1:1:1, 2:1:1, and 1:2:1), with the volume fraction between 0.5 % and 1.25 %. This study also includes a discussion of the density prediction analysis. The result shows that at a temperature of 25 °C and a volume fraction of 1.25 %, the maximum density is determined to be 1165 kg/m3. The Random Forest algorithm gives the best prediction accuracy with an R2 value of 0.928.

Abstract In harsh operating environments such as mines and gas stations, intrinsically safe power converters are required for electronics devices. Reliable communication is the key ensuring good performance of monitoring and coordinated control for intrinsically safe converters. This paper proposes a power and signal dual modulation (PSDM)‐based communication method for intrinsically safe parallel‐connected Buck converters. The bidirectional communication between two parallel Buck converters is realized by combining the frequency shift keying (FSK) and phase shift keying (PSK) methods. In addition, switching frequencies are employed to identify the signals received from the power converters interfacing different sources. According to the intrinsic safety requirement, inductors and capacitors are redesigned based on the modulated voltage ripples during communication. Simulations and prototype experiments are performed to verify the feasibility of the proposed bidirectional communication for intrinsically safe converters. The amplitude of voltage ripples modulated for carrying data is restricted to less than 1.5% of the bus voltage. The transmission speed can be achieved from 0.5 to 2kbps depending on the adopted switching frequencies. The demodulation delay is less than 5% duration of one bit of transmitted data.

In order to extend motor operation range, a self-voltage-boosting AC motor drive is proposed in this paper. Compared with the traditional frontend DC-DC converters, the proposed topology reuses the power switches inherently involved within the three-phase inverter without using any additional active components. By modifying the time distribution of the two zero vectors in traditional seven-segment space vector pulse width modulation (SVPWM) technology, the inductor current and the flying capacitor voltage can be adjusted. The proposed topology has the advantage of a wider operating range without extra cost. Moreover, the performance of the motor drive is not affected. Experimental results demonstrate the correctness of the proposed topology.

The urgent need to mitigate the severe environmental impacts of climate change necessitates a transition to a low-carbon energy infrastructure, crucial for decarbonization and achieving global sustainability goals. This study investigates the decarbonization trajectories of five major economies and significant carbon emitters: the United States of America (USA), China, Japan, Germany, and India. We focus on evaluating two decarbonization scenarios for power generation. Scenario 1 explores the use of a generic storage system for reducing critical excess electricity production (CEEP), maintaining the same thermal power plant capacity as in the reference year 2021. In contrast, Scenario 2 models thermal power plants to meet the exact electricity demand without introducing a new electricity storage system. The primary aim is to assess the feasibility and implications of achieving a 100% share of renewable and nuclear energy by 2030 and 2050 in these countries. EnergyPLAN software was utilized to model and simulate the electricity systems of these countries. The two scenarios represent different degrees of renewable energy integration, demonstrating possible transitional pathways towards an environmentally friendly electricity generation system. The study provides a comparative analysis of the outcomes for each country, focusing on carbon emissions reduction and the impact on annual total costs in 2030 and 2050. Results show that by 2030, China could reduce its emissions by 88.5% and 85.14% in Scenarios 1 and 2, relative to 2021 levels. From the two scenarios considered in all the countries, India records the highest percentage reduction while Germany has the least percentage emission in reference to 2021, with a potential decrease of 90.63% and 52.42% respectively. By 2050, carbon emissions in the USA will be reduced by 83% and 79.8% using Scenario 1 and Scenario 2 decarbonization pathways. This research significantly contributes to understanding the decarbonization potential of global electricity generation. It provides vital data for policymakers, energy planners, and stakeholders involved in developing sustainable energy policies.

Abstract The widely tunable and high resolution mid-infrared laser based on a BaGa4Se7 (BGSe) optical parametric oscillator (OPO) was demonstrated. A wavelength tuning range of 2.76–4.64 μm and a wavelength tuning resolution of about 0.3 nm were obtained by a BGSe (56.3°, 0°) OPO, which was pumped by a 1064 nm laser. It is the narrowest reported wavelength tuning resolution for BGSe OPO, and was obtained by simultaneously controlling the angle and temperature of BGSe. Graphical Abstract

A multi-port energy router (MER) is an important infrastructure for power management and energy storage after an unexpected power outage. In addition, MERs can relate to various emergency electric power sources (EEPSs) and power grids at the same time. Moreover, by putting an MER in mobile energy storage, an MER allows for more flexible energy management and regulation in urban cities. This paper proposes a new topology of MERs composed of a bidirectional AC/DC converter and a partial power processing-based triple active bridge (PPP-based TAB) converter to supply emergency power for an unexpected power outage. This design of an MER improves the power efficiency significantly to realize peer-to-peer (P2P) trading which realizes the bidirectional power transmission among the main power grid, EEPSs such as the battery of electric vehicles (EV), and clients’ loads. The control methods of the power transmission of two power electronic converters in MER are also illustrated in this paper to realize the power transmission among the main power grid, clients’ loads, and EEPSs. This allows any power source connected to the MER to be used as an input or output to receive or send out power, which greatly improves the utilization of power. In conclusion, the MATLAB/Simulink and experiment test are executed and meticulously presented to validate the practicality of the proposed configuration.

For permanent magnet (PM) machines, analysis of armature magnetic field (AMF) plays an essential role in the qualitative analysis of machine operation principle, quantitative calculation of machine electromagnetic performance, further optimization or innovation of the machine, and so on. The winding configuration determines the primitive armature magnetomotive force (MMF), which greatly influences the AMF. In this paper, the AMF analytical model of slotless PM machines with arbitrary-phase equidirectional toroidal winding (EDTW) is derived, and the harmonic characteristics of the AMF are analyzed. Firstly, the expression of the arbitrary-phase EDTW primitive armature MMF is derived, and the band factor is introduced to represent the influence of coil bandwidth on the MMF. Secondly, the AMF analytical model is derived by introducing the concept of ideal air-gap permeability density, and the harmonic characteristics of the AMF are analyzed. Finally, taking 3, 4, 5, and 6-phase EDTW as examples, the relevant conclusions obtained by the analytical method (AM) are compared with those obtained by the finite element method (FEM). The comparison results show good consistency, confirming the accuracy and effectiveness of the AM. The relevant conclusions of this paper can provide a reference for the further research and application of the EDTW.

Abstract Reflective tomography Lidar is long‐range, high‐resolution imaging Lidar. Because the angular resolution is independent of detection range, it enjoys promising application prospects in imaging of small space targets, estimation of barycentre range of space debris, and many other fields. In practice, images generated by reflective tomography Lidar generally contain a large number of artefacts and noise that need to be removed to obtain the target profile. To improve the quality of the target profile, an algorithm is proposed for the extraction and segmentation of the target region in reflective tomography Lidar images. According to the experimental results, the algorithm can achieve better segmentation results than the traditional threshold segmentation algorithms. In particular, the algorithm can maintain good segmentation results for those images with noticeable ring artefacts, strip artefacts, and noise while avoiding under‐segmentation or over‐segmentation. It also guarantees the integrity of the target segmentation, preserves the outer contour and detailed structure information of the target as much as possible, and improves the accuracy of the target segmentation. Compared with conventional threshold segmentation algorithms, the algorithm improves the quality of image segmentation, and can improve the quality factor by more than 3%.

This paper investigates the failure mechanism of the rear drive shaft in a modified pickup truck which had operated for about 3000 km. The investigation included macroscopic and microscopic evaluation to document the morphologies of the fracture surface, measurement of the material composition, metallographic preparation and examination, mechanical testing, and finite element modelling and calculations. The results obtained suggest that rotation-bending fatigue was the primary cause of the drive shaft failure. The crack initiation is located in the root of the machined threads on the drive shaft surface and expanded along the side of the machining line surface. The main cause of fatigue cracks is attributable to a high stress concentration owing to a large unilateral bending impact under overload. Meanwhile, the bidirectional torsional force also produces a higher stress concentration and thus accelerates the fatigue crack to expand radially along the surface. Furthermore, the hardness of the central section of the drive shaft was marginally below standard. This deficiency results in harm to the bearings and other mechanical components, as well as expediting the enlargement of cracks. Finite element analysis revealed significant contact stress between the bearing and drive shaft, with stress levels exceeding the fatigue limit stress of the parent material. This highlights the need for reevaluation of the heat treatment process and vehicle loading quality to enhance the drive shaft’s longevity.

The affordability and availability of water and energy have a huge impact on food production. Research has shown that there exists a direct and indirect link between power production and clean water generation. Hence the inclusion/importance given to the energy-water-food (EWF) nexus in the United Nations' sustainable development goals. Acknowledging the importance of decarbonization to the global future, there exists a gap in literature on the development of models that can enhance the EWF nexus, reduce energy poverty, and achieve 100% renewable energy in the electricity sector. Therefore, the technical and economic prospect of geothermal energy for bridging the aforementioned gaps in existing works of literature is presented in this study. The energy poverty/wealthy status of a country has been confirmed to have a significant impact on economic development, as economic development is largely reflected in the food-water availability. Ditto, this study is focused on the interconnectivity of the EWF nexus while incorporating global decarbonization targets. Geothermal energy is of the utmost significance in East Africa due to its abundant potential and distinctive geological features. Located in the Great Rift Valley, the region has an abundance of geothermal reservoirs, making it an ideal location for geothermal power generation. This study is novel as a comprehensive assessment framework for energy poverty is developed and innovative models utilizing primarily the geothermal resource in the East African region to mitigate this problem are proposed and analyzed. The role of hydrogen generation from critical excess electricity production is also analyzed. The East Africa region is considered the case study for implementing the models developed. A central renewable energy grid is proposed/modelled to meet the energy demand for seven East African countries namely; Ethiopia, Tanzania, Uganda, Djibouti, Comoros, Eritrea, and Rwanda. This study considers 2030, 2040, and 2050 as the timestamp for the implementation of the proposed models. The hybrid mix of the biomass power plant, solar photovoltaic (PV), pumped hydro storage system and onshore wind power is considered to furthermore show the potency of renewable energy resources in this region. Results showed that the use of geothermal energy to meet energy demands in the case study will mitigate energy poverty and enhance the region's EWF.

Abstract This study presents a fault coil localisation approach for a direct‐drive permanent magnet synchronous motor (DDPMSM) with inter‐turn short‐circuit fault (ISF) using the search coil (SC) array. The SCs are wound around specific stator tooth of DDPMSM through the proposed SC array arrangement principle, which reduces the invasiveness of the fault coil localisation method. The SCs are classified as several search coil groups (SCGs), the differential between SC back electromotive forces (EMFs) in the same SCGs (δ1) and SC residual back EMF (δ2) are used as fault indicators for ISF localisation. The peak values extracted from δ1 and δ2 are the basis for diagnosing ISF. Hence, the proposed approach does not need a complicated signal processing algorithm, and it is less complex. First, the SC back EMF analytical model is developed to investigate the mapping relationship between the back EMF of each SC and fault coil location. Second, fault coil localisation indicators for ISF are analysed based on the proposed analytical model, and the exact ISF localisation approach is presented. Finally, two basic installation units and the arrangement principle of SC array in DDPMSM are proposed. Simulation and experimental results justify the effectiveness of the selected fault indicators and the feasibility of the proposed SC array for diagnosing ISF.

Due to the special hydrogeological features of the sea islands, the distribution of groundwater is difficult to assess. In order to improve the accuracy of the predicting groundwater potential assessment(GPA) in islands region. We tried to use the convolutional neural networks(CNN) and took lithology, aspect, slope, water density, vegetation fraction, soil humidity and land surface temperature as the remote sensing assessment indicators. The island area of Pearl River Estuary, China was selected as the study area. The groundwater situation of Wailingding Island was taken as the sample data of the total area, and the map of this island was divided into 24 × 22 grids. The levels of GPA of each small grid was determined by hydrogeological maps with results of geophysical method and wells or springs data. Meanwhile, the corresponding data were enlarged and the sample label data set was made. Using the CNN model of learning, training and testing, analysis of groundwater potential and each coupling correlation between remote sensing indicators constantly. After 1500 times of training, loss of model dropped to 0.3113, the accuracy of model was 96.96%. A good 5 levels classification prediction of GPA model was received. The AUC of ROC curve and significance level (P) of the CNN model were 0.855 and 0.001 respectively, which were better than the results of the classic GRSFAI model. The results showed that the prediction of GPA based on CNN model can effectively assess the groundwater distribution levels in the Pearl River Estuary island area, which can provide a certain reference value or a GPA model for the other water-deficient islands. Moreover, the obtained sample set of groundwater volume distribution of bedrock islands can be used as a valuable data source for further in-depth research.

Abstract This work investigates the impact of cracks and fractural defects in solar cells and their cause for output power losses and the development of hotspots. First, an electroluminescence (EL) imaging setup was utilized to test ten solar cells samples with differing crack sizes, varying from 1 to 58%. Our results confirm that minor cracks have no considerable effect upon solar cell output, and they develop no hotspots. However, larger cracks can lead to drastic decreases in the output power, close to − 60%. Furthermore, as the crack area increased, there was a further increase in the cell's temperature under standard test conditions. On the contrary, no hotspots were found for the solar cells affected by significant creak areas (crack percentage > 46%) because there were insufficient areas to develop a hotspot. Last, a comparative analysis with solar cells affected by potential induced degradation (PID) was made. We found a strong relationship in the output power losses, and the PID test critically impacted the cells by developing localized hotspots at a temperature level close to 50 °C.

Compared with conventional phosphor‐converted near‐infrared light‐emitting diodes (NIR pc‐LEDs), near‐infrared persistent phosphor‐converted LEDs (NIR ppc‐LEDs) are applicable not only in conventional systems but also novel devices in the fields of intelligent security, driverless vehicle technology, and virtual reality. However, NIR ppc‐LEDs have not been extensively investigated. Herein, a novel NIR ppc‐LEDs with the prepared SrAl12O19: Fe3+, Mg2+, Ti4+ NIR persistent phosphors is designed. In relation to pc‐LEDs, the new ppc‐LED exhibits full‐spectral responsive sensing ability during the photostimulated mediation of lattice defects. Furthermore, using the ppc‐LED as a smart sensing LED in a digital NIR imaging system, optical feedback could be converted to a digital signal in a self‐regulated visible‐to‐NIR operation mode. These results imply that the designed ppc‐LED, as a smart sensing LED with full‐spectral responsive ability, greatly enhances the sensitivity of digital NIR imaging, particularly low‐light digital NIR imaging, which has recently drawn considerable attention in the fields of advanced driver assistance systems and VR/AR technology.

In this paper, an optimized model based on the ballistic cluster–cluster aggregation model is proposed to study the optical properties of aggregated particle structures. The critical improvement of the optimized model is the ability to arbitrarily select the original number of particles in the simulation and set different sizes of particles, whereas the original model is limited to 2n particles. Herein, the discrete dipole approximation method was used to calculate the optical extinction properties of the aggregation structure. First, the effect of porosity, which is a significant parameter, is explored, and acceptable error values are calculated. Second, simulations are performed using the optimized model for conditions applicable to the original model (N = 2n), and the difference between the two model calculations is assessed. Finally, the extinction performance of the aggregate with an arbitrary number of particles (N ≠ 2n) simulated by the optimized model is calculated and compared with the results obtained by the interpolation method. The numerical results verify the generalizability and accuracy of the optimized model.

Abstract In terms of the characteristics of multi‐objective and interactions among optimization objectives of the tubular permanent magnet linear generator with 120° phase belt toroidal winding (120°‐TPMLG), a multi‐objective optimization method is proposed to improve the generators performances, which is based on the combination of response surface method and the genetic algorithm. First, the sensitivity analysis of different structural parameters on the performances of the 120°‐TPMLG is conducted to pick out the sensitive structural parameters. Then develop those sensitive parameters as optimization variables to establish the response surface equation of the generator performances including output power (P), detent force (F), and the efficiency (η). Subsequently, based on the surface equation, the genetic algorithm (GA) fitness function is proposed to conducted the global optimization and the optimization results are finally obtained. To verify the effectiveness of the proposed optimization method, the performances of the optimal 120°‐TPMLG are analysed and compared with the initial one. The results show that the performances including the detent force and power density of the 120°‐TPMLG are greatly improved, which prove that the proposed multi‐objective optimization method is effective for the 120°‐TPMLG.

The converter switching in variable-frequency drives can generate high frequency common mode voltage between the machine winding and the converter ground, leading to high frequency parasitic currents which can flow through the machine bearings unless precautions are taken in design and installation. These parasitic and unintended currents in the bearings cause deterioration of the lubrication film and surface damage to the rolling elements of the bearings. These problems will be exacerbated as wide-bandgap semiconductors with faster switching rise times start becoming more widespread in variable-frequency drives. This paper reviews the modelling and mitigation techniques of high frequency bearing currents in inverter fed AC drives. It aims to provide a solid base for the research community to further understanding the bearing currents phenomenon and helping to find novel improved technique for their mitigation and measurement.

Electric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to comprehensively summarize both the feature engineering methods and artificial intelligence (AI) algorithms (including machine learning, neural networks and deep learning) in electric powertrain condition monitoring and fault diagnosis approaches. Specifically, this paper systematically divides the AI-supported method into two main steps: feature engineering and AI approach. On the one hand, it introduces the data and feature processing in AI-supported methods, and on the other hand it summarizes input signals, feature methods and AI algorithms included in the AI method in cases. Therefore, firstly this review is to guide how to choose the appropriate feature engineering method in further research. Secondly, the up-to-date AI algorithms adopted for powertrain health monitoring are presented in detail. Finally, such current approaches are discussed and future trends are proposed.

Abstract The three‐phase permanent magnet synchronous motor (three‐phase PMSM) has gained much popularity in many applications due to its various advantages. In addition, due to the good fault tolerance, simple inverter circuit structure and high torque density, the six‐phase PMSM has better prospects than three‐phase PMSM in biomedical devices, electric vehicles and other fields with high reliability requirements. In this study, a six‐phase axial‐flux PMSM inherently without cogging torque is proposed to improve torque density, which equips the 60° phase‐belt toroidal winding (60°TW) and slotless stator core. Based on the same effective volume, permanent magnet material, power grade and the other major motor parameters, two six‐phase PMSMs with 30° phase‐belt toroidal winding (30°TW) and 60°TW are designed, respectively. The armature reaction field, air gap magnetic density, back‐electromotive force, output torque, loss characteristic and torque‐speed characteristic are compared by using three‐dimensional (3D) finite‐element models in detail. Finally, the experimental test is carried out using the prototype of three‐phase PMSM with 120° phase‐belt toroidal winding. The experimental results indirectly verify the feasibility of 60° phase‐belt toroidal winding and correctness of 3D FEM. The 3D FEM analysis results are presented to show that torque density of the proposed six‐phase PMSM with 60°TW is improved in comparison to the six‐phase PMSM with 30°TW. Index Terms—60° phase‐belt toroidal winding, axial flux, six‐phase permanent magnet synchronous motor, torque density

The security of powering systems has been a major problem over the last decade, leading to an increased interest in wireless power and data transfer. In this research paper, a new inductive Wireless Power Transfer (WPT) circuit topology has been used. In traditional WPT circuits, the inverters are used to produce an oscillation for the transmitter coils. The classic WPT system includes intrinsic energy dissipation sources due to the use of switches, necessitating the need of an extra control circuit to ensure proper switching time. Furthermore, they have limited data encryption capabilities. As a result, an unique WPT system based on memristors has been developed, eliminating the need for switches. Furthermore, because this novel topology communicates a synchronised chaotic behaviour, it becomes highly beneficial. This circuit may be used in Near Field Communication (NFC), where chaotic true random numbers (TRNG) can be generated to increase security. The results of simulations indicate the functioning of the Memristor-based WPT (M-WPT) and its ability to generate random numbers. We experimentally proved the chaotic behaviour of the circuit and statistically demonstrated the development of the TRNG, using an Arduino board and the Chua circuit to build the M-WPT circuit.

A photon-counting distributed free-space spectroscopy is proposed and demonstrated using lidar technique. It is suitable for remote spectrum analysis with range resolution over wide band.

Abstract A permanent magnet synchronous motor (PMSM) is introduced with 120° phase belts toroidal windings (PBTWs) (120°PBTWPMSM), in which its armature reaction field principle is elaborated. Then, the experiment concerning the 120°PBTWPMSM is conducted to verify the validity of the related analysis and the feasibility of the 120°PBTWPMSM. In addition, a method of shifting the magnetic poles (SMP) is adopted to suppress the cogging torque and the torque ripple. After this, the 120°PBTWPMSM after SMP is further optimised by the Taguchi method to obtain the structural parameters of the motor with optimal comprehensive performance. Finally, the performance of the original motor, the 120°PBTWPMSM after SMP and the optimal motor are compared, which verify the effectiveness of the optimization methods.

Objectives: Comparative studies of different smokescreen designs are essential to determine differences in extinction performance. This study aims to investigate the extinction performance of explosive smokescreen under different conditions, and to provide an evaluation method for the optimal design of its charge structure. Methods: The process of formation of the smokescreen with a cylindrical charge structure is described based on the smoothed particle hydrodynamics method. The blast radius and particle density distribution of the smokescreen were calculated for different charge structures and charge ratios through simulations. Lambert–Beer's law was combined to obtain the infrared extinction area. An analysis was then conducted to determine the influence of the number of baffles in the charge structure and charge ratio on the extinction performance of the smokescreen. Field tests were conducted to verify the simulation results. Results: Increasing the number of baffles in the projectile structure made the particle distribution of the smokescreen more uniform and resulted in a larger infrared extinction area. An increase in the explosive quantity, made the smokescreen more dispersed. However, too much of the explosives caused the smokescreen to be sparse, reducing the infrared extinction area.

Black soybean (Glycine max (L.) merr), as an edible seed of the leguminous plant, possesses a high amount of proteins and carbohydrates. However, few studies have been conducted on the protein-polysaccharide polymer (proteoglycan) in Black soybean. Here, we aim to characterize the proteoglycan in Black soybean. Firstly, a crude extract from Black soybean was obtained by water extraction and alcohol precipitation. Then, DEAE-52 cellulose and Sephadex G-100 column chromatography were used to isolate proteoglycans in the crude extract, and a novel proteoglycan named BBWPS2 was obtained. Moreover, the chemical analysis showed that BBWPS2 was a proteoglycan of 2.02 × 105 Da, and composed of 7 kinds of monosaccharides and 16 kinds of amino acids. β-elimination reaction identified the carbohydrate chain was connected to peptide chain with O-glycosidic bonds. Finally, the immunomodulatory tests indicated that BBWPS2 can significantly promote the primary splenocyte proliferation and activate mouse peritoneal macrophages to produce TNF-α. Subsequent pathway studies revealed that BBWPS2 activated macrophages through the mannose receptor-mediated NF-κB/MAPK signaling pathway. Our work indicated that BBWPS2 from Black soybean could be explored as a promising natural immunopotentiator in food and pharmaceutical industries.

Rhizoma Acori Tatarinowii is a traditional Chinese herb used to treat depression and coronary heart disease. Studies on its active components mainly focus on small molecular compounds such as asarone and other essential oil components, while the large molecular active components such as polysaccharides are ignored. In this study, we aimed to study the chemical structure and immune activation of polysaccharides from Rhizoma Acori Tatarinowii. In this study, a polysaccharide (RATAPW) was isolated and purified by DEAE-52 cellulose and Sephadex G-100 column chromatography from alkali extraction polysaccharide of Rhizoma Acori Tatarinowii. The average molecular weight of RATAPW was 2.51 × 104 Da, and the total carbohydrate contents of RATAPW were 98.23 ± 0.29%. The monosaccharide composition, methylation, and nuclear magnetic resonance (NMR) analysis results displayed that the polysaccharide was α-1,4-glucan with short α-1,6 branches. Immunofluorescence assay and inhibitor neutralization assay indicated that RATAPW could promote the TNF-α production of RAW264.7 macrophage through the nuclear factor kappa B (NF-κB) molecular signaling pathway. Treatment with 200 μg/ml of RATAPW enhanced a 38.77% rise in the proliferation rate of spleen lymphocytes. RATAPW also enhances ConA-induced T cells and lipopolysaccharide (LPS)-induced B cell proliferation in a dose-dependent effect. Our study lays a foundation for the discovery of natural polysaccharide immune modulators or functional food from Rhizoma Acori Tatarinowii.

Artificially prepared microbial spores have excellent electromagnetic attenuation properties due to their special composition and structure. At present, studies on the optical properties of microbial spores have mainly focused on those with a single band or a single germplasm, which has limitations and cannot reveal the optical properties comprehensively. In this paper, 3 kinds of laboratory-prepared microbial spores were selected for compounding, and the spectral reflectivities of single-germplasm biospores and compound biospores were measured in the wavebands of 0.25–2.4 and 3–15 μm. The complex refractive indices (CRIs) were calculated in combination with the Kramers–Kronig (K-K) algorithm. Relying on the smoke box broadband test system, the transmittance of single-germplasm bioaerosols and compound bioaerosols from the ultraviolet (UV) band to the far-infrared (FIR) band was measured, and the mass extinction coefficients were calculated. The results indicate that the trend of the complex refractive indices of the compound spores is consistent with that of the single-germplasm spores with a larger particle size. For the single-germplasm bioaerosols, the lowest transmittance values were 2.21, 5.70 and 6.27% in the visible (VIS), near-infrared (NIR) and middle-infrared (FIR) bands, and the mass extinction coefficients reached 1.15, 0.87 and 0.84 m2/g, respectively. When AO and BB spores were compounded at 4:1, the extinction performance of the bioaerosols somewhat improved in all wavebands. These results can help to comprehensively analyze the optical properties of bioaerosols and provide ideas for the development of new extinction materials.

The impact of electromagnetic interference (EMI) is an increasingly important aspect of the performance of switching inverters. The challenges of managing EMI continue to grow with the emergence of wide bandgap (WBG) devices, the trend towards ever-greater integration and higher power rating. This paper reviews suppression methods for the conductive common-mode (CM) EMI in inverter fed motor drives. In order to span EMI suppression across the full system design process, the review considers both mitigation from the sources and suppression along the conduction paths. Furthermore, the shortcomings and merits of the reviewed publications are discussed, and their attenuation frequency range and attenuation level are compared. It is demonstrated that the CM EMI at low frequency is mainly determined by the PWM strategies and can be reduced or even theoretically eliminated through zero common-mode control. On the other hand, the CM EMI at high frequency is markedly influenced by the switching transients of the power devices. Thus, various drive circuits are reviewed which improve the switching behavior. Finally, the deployment of passive and active filters to suppress or compensate for the EMI is discussed.

There are several non-uniform effects on photovoltaic (PV) modules related to aging in a PV array. These subsequently bring about non-uniform operating parameters with individual PV modules, causing a variance in the PV array performance. The current study undertakes an indoor experimental study to establish and positively affect the efficacy of a non-uniform aged 2 $\times 4$ PV array, with a commercially available small panel module of 0.36W (monocrystalline). This paper proposes a gene evolution algorithm (GEA) for offline reconfiguration that can provide more significant output power compared to non-uniformly aged PV arrays through repositioning instead of replacing aged PV modules, which will help lower maintenance expenses. This reconfiguration requires data input from the PV module’s electrical properties in order to select ideal reconfiguration setups. The outcomes show that greater output power can be facilitated through a non-uniformly aged PV array and used on many different PV array sizes.

To lower the difficulty of fault protection, a doubly-fed induction machine based shipboard propulsion system (DFIM-SPS) that is partially power decoupled is presented. In such an intrinsically safe SPS architecture, a synchronous generator (SG) is employed for power generation, and the accuracy of the parameters of power generation unit (PGU) plays an important role in SPS stable operation. In this paper, the PGU parameter deviations are studied to evaluate the effects on system performance. The models of salient-pole SG, type DC1A excitation system (EXS) and DFIM are illustrated first. Besides, the corresponding control scheme is explained. For the 16 important parameters of PGU, up to 40% of parameter deviations are applied to implement parameter sensitivity analysis. Then, simulation studies are carried out to evaluate the parameter deviation effects on system performance in detail. By defining three parameter deviation effect indicators (PDEIs), the effects on the PGU output variables, which are the terminal voltage and output active power, are studied. Moreover, the increasing rates of PDEIs with different degrees of parameter deviations for the key parameters are analyzed. Furthermore, the overall system performance is investigated for the two most influential PGU parameters. This paper provides some vital clues on SG and EXS parameter identification for DFIM-SPS.

Power & signal multiplex transmission (P&SMT) is a technique that uses power electronic circuits for communication signal transmission. In this paper, a three-phase cascaded multilevel inverter-based P&S MT system is proposed. The proposed method can transmit communication signals without using a Controller Area Network bus, thereby reducing the wiring cost of the conventional electric vehicle (EV) communication system. The designed system can achieve motor speed regulation and battery balance discharging for EVs. With the combined pulse width modulation scheme and frequency shift keying method, both power and communication signals are transmitted successfully in a simulation model implemented in Matlab/Simulink. By evaluating the bit error rate of the transmitted signal, the maximum signal rate of the proposed system is determined as 600 bit/s.

This paper develops a high-performance finite control set model predictive current control (FCS-MPCC) method for induction motors (IM) to ensure the system control performance over the low-switching-frequency (LSF) range, where the switching loss is low. The new controller is based on tripartite calculations in each control period and sliding mode (SM) rotor-related inductance observer. In detail, firstly, to solve the problem that the control performance of the traditional FCS-MPCC methods is low when they are used at low frequencies, tripartite calculations are employed in each control period to improve the prediction accuracy. By dividing one control period into three equal parts and executing the prediction algorithm in each subsection, the current estimation errors become lower compared to the conventional single-step Euler-discretization controller. Then, considering that the performance of an FCS-MPCC controller highly relies on the machine parameter values, but it is hard to directly obtain the accurate rotor-related inductances (mutual inductance and rotor inductance), this paper uses an online parameter observer based on the sliding mode (SM) principle to diagnose them in real time. It needs to be mentioned that when discussing the stability of the sigmoid-function-based SM observer, a new technique called estimation-error-limitation is initially adopted in this paper to simplify the analysis process. Finally, the proposed algorithms are verified by simulation, which is conducted on a three-phase IM at LSF situations.

In the past decades, a huge number of large-scale photovoltaic (PV) plants have been constructed all around the world. However, the capital and operational costs can be high, which limits their widespread applications. Because the installed PV modules often operate in harsh environments (i.e. storm, high temperature, dust, hail, etc.), non-uniform aging phenomena of PV modules cannot be avoided and it impacts adversely on the performance of PV plants, especially in the middle and late periods of their service life. This paper develops an offline PV module reconfiguration strategy for the non-uniform aging PV array to mitigate this effect. In order to maximize the economic benefit, electricity price and labor cost are considered in the offline reconfiguration. The Branch-and-Bound based optimization algorithm is proposed to find the highest economic benefit. In order to verify the proposed algorithm, MATLAB software-based modelling and simulation have been performed in four case studies, in which a typical 42kW PV array with 7 × 20 connection is employed in a testing benchmark, and the manpower cost and electricity price in USA, UK, China and Turkey are considered in the case study. The results demonstrate that the economic benefit from a non-uniformly aged PV array is further enhanced with the proposed reconfiguration method.

Due to the wide applications of solar photovoltaic (PV) technology, safe operation and maintenance of the installed solar panels become more critical as there are potential menaces such as hot spot effects and DC arcs, which may cause fire accidents to the solar panels. In order to minimize the risks of fire accidents in large scale applications of solar panels, this review focuses on the latest techniques for reducing hot spot effects and DC arcs. The risk mitigation solutions mainly focus on two aspects: structure reconfiguration and faulty diagnosis algorithm. The first is to reduce the hot spot effect by adjusting the space between two PV modules in a PV array or relocate some PV modules. The second is to detect the DC arc fault before it causes fire. There are three types of arc detection techniques, including physical analysis, neural network analysis, and wavelet detection analysis. Through these detection methods, the faulty PV cells can be found in a timely manner thereby reducing the risk of PV fire. Based on the review, some precautions to prevent solar panel related fire accidents in large-scale solar PV plants that are located adjacent to residential and commercial areas.

The new technology solutions are playing an important role in the hardware security. One of the latest techniques is the use of the Memristor as an encryption element. In this paper, it has been introduced a two-column array inductor for Memristor-Based Wireless Power Transfer (M-WPT) systems. The traditional WPT circuits are based on switches, which do require a control circuit for timing and have low data encryption factor. By adopting the memristive Chua`s circuit with the chaotic behaviour characteristic, it is possible to create a symmetrical dual-key cryptography. Furthermore, in this innovative solution, the high inductance value of the traditional Chua circuit can be further reduced by using the dynamic effects of the coils flux linkage. The simulation results exhibit the dual-scroll attractors phase portrait, which is available for encryption features. Therefore, the data collected from the phase portrait are used as true random number generator in Python code. Instead of using algorithms, the code can create a symmetrical key for an unique chaotic cryptography. In order to build a prototype, it has been created a PCB design for the whole system. The experiment highlights the unpredictable voltage and current and validates the chaotic behaviour of the transmitter and receiver.

When the asymmetric fault occurs in power grid, it will cause the oscillation of the electromagnetic torque of the doubly-fed induction generator(DFIG). The oscillation of the electromagnetic torque will cause the torsional vibration of the drive chain, and the long-term torsional vibration will shorten the service life of DFIG. The analysis shows that the positive and negative sequence current components generated by DFIG terminals are the main factors that cause electromagnetic torque oscillation when the asymmetric faults occur. Therefore, the suppression of positive and negative sequence current components is the key to achieve active control of torsional vibration of drive chain, the traditional PID control can only adjust the direct-current(DC) component without static error, and cannot suppress the negative sequence alternating-current(AC) component. In this paper, based on the traditional PID regulator, the resonance(R) current regulator with the specific frequency is incorporated into the grid-side converter(GSC) to form the PID-R current controller. The PID-R current controller can realize accurate and effective control of the positive and negative sequence components in the forward rotation synchronous speed rotating coordinate system without decomposing the positive and negative sequence components, and can actively suppress the electromagnetic torque oscillation, thereby realize the active control of the drive chain torsional vibration. MATLAB/Simulink verifies the effectiveness of PID-R current controller.

Despite the tremendous endeavors devoted to exploiting the nature of glass transition, the factors that control the steepness index of viscosity near glass transition, that is, fragility, remain unclear. In this study, we demonstrate that, for polymeric and small molecular weight organic glass formers, fragility increases upward with increasing size of the free volume void at the glass transition temperature. This changing trend indicates that fragility is governed by the properties of the segments or molecular clusters in the free volume void rather than by the properties of the entire polymer chains. The physics behind the relationship between fragility and free volume void at the glass transition temperature is consistent with the physics behind the relationship between fragility and the molecular weight as well as the mechanics of the relationship between fragility and size of the cooperative units. This relationship also provides new insights into the understanding of the nature of the glass transition of polymeric and small molecular weight organic glass formers.

A natural α-1,6-glucan named BBWPW was identified from black beans. Cell viability assay showed that BBWPW inhibited the proliferation of different cancer cells, especially HeLa cells. Flow cytometry analysis indicated that BBWPW suppressed the HeLa cell cycle in the G2/M phase. Consistently, RT-PCR experiments displayed that BBWPW significantly impacts the expression of four marker genes related to the G2/M phase, including p21, CDK1, Cyclin B1, and Survivin. To explore the molecular mechanism of BBWPW to induce cell cycle arrest, a transcriptome-based target inference approach was utilized to predict the potential upstream pathways of BBWPW and it was found that the PI3K-Akt and MAPK signal pathways had the potential to mediate the effects of BBWPW on the cell cycle. Further experimental tests confirmed that BBWPW increased the expression of BAD and AKT and decreased the expression of mTOR and MKK3. These results suggested that BBWPW could regulate the PI3K-Akt and MAPK pathways to induce cell cycle arrest and ultimately inhibit the proliferation of HeLa cells, providing the potential of the black bean glucan to be a natural anticancer drug.

Measurement of the degree of polarization of backscattering light from rough surfaces plays an important role in targets-detection applications. The polarization bidirectional reflectance function is the key to establish the relation between the polarization states of incidence and backscattering light. For the purpose of obtaining a polarized bidirectional reflectance distribution function (pBRDF) of a realistic, complicated target, it is decomposed as typical geometric surfaces and analytically calculated as the degree of polarization of the backscattering light, using a microfacet model, under conditions in which the scale of the target is far less than the target distance. In an experiment testing several typical geometric models, the results coincided with the theoretical calculation. The degree of polarization varied substantially as the rotation angle of the target changed, but showed little dependence on the size of target. The results have potential in applications discriminating between targets at different spatial orientations.

The 101-channel full-waveform hyperspectral LiDAR (FWHSL) is able to simultaneously obtain geometric and spectral information of the target, and it is widely applied in 3D point cloud terrain generation and classification, vegetation detection, automatic driving, and other fields. Currently, most waveform data processing methods are mainly aimed at single or several wavelengths. Hidden components are revealed mainly through optimization algorithms and comparisons of neighbor distance in different wavelengths. The same target may be misjudged as different ones when dealing with 101 channels. However, using the gain decomposition method with dozens of wavelengths will change the spectral intensity and affect the classification. In this paper, for hundred-channel FWHSL data, we propose a method that can detect and re-decompose the channels with outliers by checking neighbor distances and selecting specific wavelengths to compose a characteristic spectrum by performing PCA and clustering on the decomposition results for object identification. The experimental results show that compared with the conventional single channel waveform decomposition method, the average accuracy is increased by 20.1%, the average relative error of adjacent target distance is reduced from 0.1253 to 0.0037, and the degree of distance dispersion is reduced by 95.36%. The extracted spectrum can effectively characterize and distinguish the target and contains commonly used wavelengths that make up the vegetation index (e.g., 670 nm, 784 nm, etc.).

Low Earth orbit satellite constellation networks (LSCNs) have attracted significant attention around the world due to their great advantages of low latency and wide coverage, but they also bring new challenges to network security. Distributed denial of service (DDoS) attacks are considered one of the most threatening attack methods in the field of Internet security. In this paper, a space-time graph model is built to identify the key nodes in LSCNs, and a DDoS attack is adopted as the main means to attack the key nodes. The scenarios of two-satellite-key-node and multi-satellite-key-node attacks are considered, and their security performance against DDoS attacks is also analyzed. The simulation results show that the transmission path of key satellite nodes will change rapidly after being attacked, and the average end-to-end delay and packet loss are linearly related to the number of key-node attacks. This work provides a comprehensive analysis of the security performance of LSCNs under a DDoS attack and theoretical support for future research on anti-DDoS attack strategies for LSCNs.

Lycopi Herba has been broadly used as a traditional medicinal herb in Asia due to its ability to strengthen immunity. However, it is still obscure for its material basis and underlying mechanisms. Polysaccharide, as one of the most important components of most natural herbs, usually contributes to the immunomodulatory ability of herbs. Here, we aimed to detect polysaccharides from Lycopi Herba and examine their potential immunomodulatory activity. A novel polysaccharide (LHPW) was extracted from Lycopi Herba and purified by DEAE-52 cellulose chromatography and G-100 sephadex. According to physicochemical methods and monosaccharide composition analysis, LHPW was mainly composed of galactose, glucose, fructose, and arabinose. NMR and methylation analyses indicated that LHPW was a neutral polysaccharide with a backbone containing →3,6)-β-D-Galp-(1→, →4)-β-D-Galp-(1→ and →4)-α-D-Glcp-(1→, with the branches of →1)-β-D-Fruf-(2→ and →6)-α-D-Galp-(1→. Immunological tests indicated that LHPW could activate macrophage RAW264.7 and promote splenocyte proliferation. This study discovered a novel polysaccharide from Lycopi Herba and showed it was a potential immunomodulator.

Condition monitoring (CM) is widely used in wind turbines (WTs) to reduce operation and maintenance (O&M) costs. Bearings are crucial components in WT and many bearing CM approaches have focused on vibration analysis. Statistical theory and artificial intelligence-based WT bearings evaluation methods require mass data for training, which makes the detection of incipient failures barely possible. In this paper, a WT bearing performance evaluation method is proposed based on the similarity analysis of fuzzy k-principal curves (FKPCs) in manifold space. For a start, 38 features are extracted from bearing vibration signals to constitute high-dimensional feature matrices. The feature matrices for the healthy samples and the samples to be evaluated are then transformed into 3-D space. Afterward, the FKPCs are extracted and the similarities among the curves of samples are calculated based on the Hausdorff distance to evaluate the performance of the bearing. Bearing degradation experiments are investigated to verify the efficiency of the proposed method. The results indicate that the proposed FKPC method can portray the degradation trend of bearings accurately with the capability of detecting incipient failures. The proposed method can be applied in the case of small-size training samples with stable performance.

This paper proposes a simple and fast fault diagnosis strategy for bidirectional isolated dual-active-bridge dc-dc converter with the extended-phase-shift control to improve reliability. The fault diagnosis strategy utilizes the average values of the voltage to quickly identify failures position and devices in either transistors or diodes without adding any hardware cost. First, two conditions are classified according to the direction of the inductor current on the primary side. Then, a comprehensive operation analysis during normal and faulty conditions is presented. Based on the characterization of open-circuit faults in diodes and transistors, open circuit faults diagnose strategy is derived and validated through both simulation and experimental test for different fault conditions.

This paper presents a review of the electrical and electronic technologies investigated in more-electric aircraft (MEA). In order to change the current situation of low power efficiency, serious pollution, and high operating cost in conventional aircraft, the concept of MEA is proposed. By converting some hydraulic, mechanical, and pneumatic power sources into electrical ones, the overall power efficiency is greatly increased, and more flexible power regulation is achieved. The main components in an MEA power system are electrical machines and power electronics devices. The design and control methods for electrical machines and various topologies and control strategies for power electronic converters have been widely researched. Besides, several studies are carried out regarding energy management strategies that intend to optimize the operation of MEA power distribution systems. Furthermore, it is necessary to investigate the system stability and reliability issues in an MEA, since they are directly related to the safety of passengers. In terms of machine technologies, power electronics techniques, energy management strategies, and the system stability and reliability, a review is carried out for the contributions in the literature to MEA.

The advancement in power electronics techniques provides a strong impetus for the adoption of medium-voltage direct current (MVDC) shipboard power system (SPS). However, high fault protection difficulty and cost are the major challenges. In this paper, a partially power decoupled SPS based on the doubly fed induction machine (DFIM) propulsion load is presented to increase the system safety level by using less power electronics. Different from a grid-connected DFIM-based system, the on-board power of the proposed DFIM-SPS is supplied from standalone synchronous generators, and its system dynamics need to be further investigated. An interface friendly average model for the back-to-back power converter (BTBPC) in DFIM-SPS is proposed for system-level dynamic study, which reduces the simulation time and is easy for physical understanding. The stator and BTBPC of DFIM are regarded as separate voltage vectors in the system, and small-signal modeling is carried out in the electromechanical control timescale to analyze the internal voltage phase-amplitude dynamics. The control effects of rotor speed control (RSC), reactive power control (RPC), and phase-locked loop (PLL) are considered in the modeling process. The simulations are performed to study the control effects on DFIM-SPS in MATLAB/Simulink, with the effectiveness of the proposed BTBPC average model validated.

This paper presents a technical overview for fault diagnosis and fault-tolerant strategies of switched reluctance machine (SRM) systems. With the widespread utilization of electrical motors, stability and reliability become the most important considerations for safety. SRMs are famous for their great robustness, wide speed range, and high fault-tolerant capability, which are much suitable for high-speed and safety-critical applications. Although the SRM drive have some fault tolerance ability, the fault still seriously affects the system performance. The faults happening in different parts, such as converter, motor winding, sensors, and rotor, may lead to low torque, large torque ripple, overcurrent, insulation damage, and even system broken-down, without any fault-tolerant consideration. Therefore, it is important and urgent to improve the motor system reliability and robustness for SRM drives. Aiming at offering novel solutions to the faults in SRM drives, this paper begins with mathematical modeling of the SRM, and then concentrates on the state-of-art fault diagnosis and fault tolerance techniques to enhance the fault ride-through ability. Two categories, containing fault diagnosis and fault tolerance technologies are emphasized for fast faults locating and stable post-fault operation. Advanced technologies are reviewed, classified, and compared comprehensively. Besides, several fault diagnosis and tolerance schemes that have been developed by authors are presented and discussed as well. Finally, the research status and outlooks on this topic are put forward. It is our target to offer a global vision on fault-related strategies and bring out promising ideas about fault-related techniques in SRM drives.