Beyond the traditional vector control and the need the strengthening integrated vector management in Latin America.

Doubly-fed induction generator (DFIG) has been widely used in variable-speed constant-frequency (VSCF) wind energy generation system. Vector control is already applied to the DFIG control, which makes the DFIG gain good performance in the wind energy capturing operation. But in the two traditional vector control schemes, the stator magnetizing current is considered invariant in order to simplify the rotor current inner-loop controller. The two schemes are capable of performing very well when the grid is in normal condition. However, when the grid disturbance, such as grid voltage dip or swell fault, occurs, the control performance will be getting worse, the rotor over current will occur, which seriously reduce the ride-through ability of the DFIG wind energy generation system. Based on the accurate model of the DFIG, the deficiency of the traditional vector control is deeply investigated. The improved control schemes of the two typical traditional vector control schemes used in DFIG is proposed, and the simulation study of the proposed and traditional control schemes is carried out. The validity of the proposed modified schemes to control the rotor current and to improve the ride-through ability of the DFIG wind energy generation system is proved by the comparison study

A200 an important industrial equipment in the field of industrial production, the problem of operating efficiency of asynchronous motor has drawn wide attention with the increasing severity of environmental problems in recent years. Intended to this problem, this paper takes the retarder test bed motor control system as the research object, deduces the motor efficiency optimization control algorithm, and uses the Simulilnk module in Matlab to establish the asynchronous motor loss model. Based on the vector control of asynchronous motor, a simulation model of efficiency optimization control based on loss model is established, and the efficiency optimization control of induction motor based on loss model is realized. At the same time, this paper compares the efficiency optimization control algorithm with the traditional vector control to test the effect of the efficiency optimization algorithm. The experimental results show that the efficiency optimization algorithm used in this paper responds rapidly and the mutation of the load torque fluctuates slightly, but it can be quickly re-stabilized. At the same time, compared with the traditional vector control, efficiency optimization algorithm can significantly improve the test bed operating efficiency. The simulation experiment provides the theoretical support and reference for the future application of the motor control system on the retarder test bench.

As one of the most important power equipment in industrial society, permanent magnet synchronous motor has a large number of uses in many fields. The traditional three-phase permanent magnet synchronous motor using vector control technology cannot meet the industrial high precision control requirements. So, the motor is analysed, the permanent magnet motor control model is constructed, and the chicken cluster algorithm is used to optimize the vector control parameters of the permanent magnet synchronous motor. Considering that the traditional cluster model faces the problem of local optimal solution, hypercube sampling and particle swarm model are used to optimize the chicken flock model, and an improved chicken flock motor control model is constructed to achieve efficient control of the motor. In the test of Rastrigin function, the particle swarm model, the traditional flock model and the improved flock model converge after 91, 72 and 20 iterations, respectively, and the best objective function values of 3.91e-4, 9.27e-5 and 1.79e-5 are obtained for the three models. The improved flock model has better convergence efficiency and convergence accuracy. In the test of different motor control methods, the overall fluctuation of the improved flock model is smaller, and the stability is improved by 31.65% compared with the traditional vector control, and 11.35% compared with the traditional flock model. The research content has important reference value for intelligent control of motors.

During the operation of permanent magnet synchronous motor, the traditional vector control method does not match the regulator parameters with the actual parameters due to the disturbance factors such as motor parameter perturbation and load change, resulting in the decline of control performance. The research object of robust control is uncertain system, which has natural advantages for improving the robustness of control system. Firstly, this paper introduces the basic principle and implementation process of robust control, simulates and analyzes the influence of motor parameters on control performance, investigates and summarizes the improvement methods of traditional vector control, and points out the advantages and disadvantages of each scheme; Secondly, the similarities and differences of the improved methods are analyzed and compared; Finally, the development trend of permanent magnet synchronous motor control technology based on robust control is prospected.

Interior permanent magnet synchronous motor (IPMSM) drives have been widely employed in sustainable transport such as electric vehicles (EV). However, the traditional vector control (VC) strategies cannot achieve optimal control due to the intrinsic property of the IPMSMs, which is strong coupling. To solve the issue, this paper proposes an improved decoupling VC strategy to improve the steady-state performance of the IPMSMs with reduced parameter mismatch impacts. First, a deviation decoupling strategy is developed, and meanwhile, the parameters that influence the decoupling method are clearly illustrated. This enriches the theory concerning decoupling control and lays the ground for the development of effective solutions to the parameter mismatch issue. Second, the Luenberger observer theory is discussed, based on which the reason why the Luenberger inductance observers are not widely employed is explained for the first time. Third, with the aid of intermediate variables, which are the disturbances caused by the mismatched inductances, a new inductance identification method based on the Luenberger observer is proposed. Finally, the simulation and experimental results prove that the proposed decoupling methods, as well as the parameter identification method, are effective.

This paper proposes a maximum torque control (MTC) strategy for hybrid excitation synchronous motor (HESM), which can achieve high-efficient controllability of air-gap flux. Due to the nonlinear relationship between field current and electromagnetic torque, it is significant to seek the optimal value of field current to obtain the maximum torque output. A field current self-optimization method based on variable step-size search theory, considering the voltage and current limit and 3-region division of the whole speed range, is presented. The flowchart of MTC according to the proposed strategy is constructed. By incorporating field current self-optimization method into traditional vector control, the coordinated control of armature current and field current is implemented, resulting in the enhancement of dynamic performance and expansion of high-speed operation range of the HESM. The simulation and analysis of the drive system with respect to a rotor magnet-shunting HESM is implemented, and the prototype HESM drive system is developed. The effectiveness of the proposed MTC strategy is verified by the simulation and experimental results.

The edge-end effect in the operation of a Linear Induction Motor (LIM) will lead to great changes in motor parameters, and the d and q axes of LIM have cross-coupling terms. The PI regulator usually used in traditional vector control has a high requirement on the accuracy of parameters, and the PI regulator cannot realize the complete axis decoupling control of d and q, which will lead to the decline of the control accuracy. In this paper, the mathematical model of the LIM considering side effects is adopted, and a complex vector regulator is used to replace the traditional PI regulator, which realizes decoupling control of the d-q axes and eliminates the influence of coupling terms. Finally, this paper is verified on dSPACE semi-physical platform. Results show that the decoupling effect of d-q axes using complex vector regulator is due to the PI regulator.

The vector control of LNG marine propulsion motor based on improved EKF is proposed for the problem of frequency operation of the controller which caused by the unknown external load disturbance that acting in the LNG marine propulsion motor system. The periodic torque ripples are reduced by introducing the speed error compensation based on improved EKF into the update algorithm of the vector control by utilizing the previous error data and error changing trend, and the results were compared with the traditional EKF vector control. It shows that the torque ripples of propulsion motor were minimized, the state observer accuracy and robustness performance of the LNG control system was improved.

BACKGROUND Insecticides play a critical role in controlling insect vectors, particularly during epidemics. Effective chemical control relies on the robust monitoring of insecticide resistance to guide evidence-based decision-making in vector control strategies.OBJECTIVES This study assessed the susceptibility of Aedes aegypti, the primary vector of dengue, Zika, and Chikungunya viruses, to various larvicides and adulticides deployed during Brazil’s national campaigns from 2020 to 2023.METHODS Mosquito collection was performed in 46 Brazilian municipalities using ovitraps. Eggs were transported to FIOCRUZ to establish the F1 and F2 generations. The Rockefeller strain was employed to determine the discriminating concentrations (DC) for the larvicide Natular™ 20EC (spinosad) and the adulticides Cielo™ (imidacloprid and prallethrin) and Fludora® Fusion (clothianidin and deltamethrin) using a modified World Health Organization (WHO) bottle bioassay. These DCs were then used to estimate the resistance status of Ae. aegypti populations in the tested formulations. Resistance intensity was assessed by exposing mosquitoes to five, 10, or 20 times the DC concentrations.FINDINGS All Ae. aegypti populations were fully susceptible to larvicide spinosad. However, resistance to both adulticide formulations was detected based on WHO criteria (mortality rates < 90%). Intensity assays revealed high to very high resistance to combined adulticide products.MAIN CONCLUSIONS Our findings indicate the full susceptibility of Ae. aegypti populations in Brazil to spinosad, but substantial resistance to adulticides used in space spraying and residual applications, likely due to pre-existing pyrethroid resistance. However, the specific contributions of each active ingredient remain unclear, owing to the evaluation of the combined formulations. The efficacy of both traditional and alternative vector control strategies must be continuously evaluated and closely monitored to ensure the real-time assessment of their performance. For chemical control, future studies should prioritise the assessment of combination products in field trials, refining laboratory assays, and sustaining insecticide resistance surveillance to optimise control efforts in Brazil.

Traditional direct vector control (DVC) compositions which consist of proportional-integral (PI) regulators of a doubly fed induction generator (DFIG) driven have several disadvantages such as parameter variation problem, low dynamic performances and poor robustness. Therefore, based on examination of the DFIG model supplied by new modulation method, this work addresses a four-level space vector modulation (SVM) based on neural networks (NSVM). The conventional DVC control with SVM strategy has large ripples on the electromagnetic torque, harmonic distortion of rotor current, stator reactive and active powers developed by the DFIG-based wind turbine systems (WTSs). In order to resolve these problems, the DVC technique with NSVM strategy is proposed. Simulation results show the effectiveness of the proposed control technique especially in electromagnetic torque, power ripples and robustness against parameters variations.

Introduction:Vector-borne diseases (VBDs) remain a significant global public health challenge, disproportionately affecting low- and middle-income countries. Traditional vector control methods, particularly chemical insecticides, face increasing limitations due to the rapid evolution of resistance and environmental concerns.Materials and methods:This review explores recent advancements in vector control, focusing on the integration of genomic, biological, and chemical strategies as innovative solutions to address these challenges. These methods include genomic tools such as CRISPR/Cas9-mediated systems, biological interventions like Wolbachia-based strategies and sterile insect techniques (SIT), and chemical innovations involving insecticides with novel modes of action and advanced delivery systems.Results:Genomic strategies like CRISPR/Cas9 gene drives show significant potential for precisely targeting vector reproduction and pathogen spread but face ecological and ethical hurdles to widespread use. Successful biological interventions, such as Wolbachia and SIT, have proven effective in reducing vector populations, yet they demand strong community involvement and ongoing funding for scalability. Additionally, innovative chemical solutions, including new insecticides and delivery methods, tackle resistance issues while reducing environmental harm, with techniques like microencapsulation and synergists improving sustainability.Discussion:This review highlights the importance of Integrated Vector Management (IVM) frameworks that combine genomic, biological, and chemical strategies. These integrated approaches maximize synergies while mitigating the limitations of individual methods. Key findings emphasize the potential of integrated approaches to achieve sustainable reductions in vector populations and disease transmission. However, significant challenges remain, including the need for standardized protocols, long-term effectiveness data, and considerations of ecological risks and climate change impacts.

The state of the art of control strategies of modular multi‐level converter based high‐voltage DC systems can be categorised into two classes according to the connecting system, i.e. the active control strategies such as the vector control and the passive control ones such as the voltage and frequency control. These two control modes need to be transformed to each other under some circumstances. The traditional method to deal with this problem is to switchover the control modes. However, this process takes time and there is risk to lose stability during this period. Thus, an extended control strategy to control the converter in both modes is wanted. An extended control strategy is designed by introducing supplementary feedback property into the traditional vector control strategy. The extended and traditional vector control strategies are compared under various cases by simulation. Simulations show that the system controlled by the traditional vector control strategy loses stability when synchronous machines drop out. However, the extended vector control strategy is superior under weak or even passive grid conditions.

The application of machine learning (ML) technologies in malaria elimination programs represents a paradigm shift in vector-borne disease control strategies. This review examines the comparative implementation of ML-based approaches in vector control programs across West Africa and historically endemic regions in the Southern United States. Through systematic analysis of recent literature, we evaluate the effectiveness of ML algorithms including support vector machines, random forests, deep learning models, and predictive analytics in malaria vector surveillance and control. Our findings reveal that while West African programs leverage ML primarily for outbreak prediction and vector habitat mapping using drone imagery and environmental data, the historical elimination success in the Southern United States provides valuable lessons for contemporary ML-enhanced programs. The review demonstrates that machine learning models such as support vector machines, decision trees, random forests, Extreme Gradient Boosting, logistic regression, K-Nearest Neighbours, Naïve Bayes, and multilayer perceptron have been greatly used to predict malaria using socioeconomic and environmental variables. Current applications show promise in drone imagery and deep learning analysis for targeted vector surveillance, enabling more precise identification of mosquito breeding sites. This comparative analysis highlights the evolution from traditional vector control methods to sophisticated ML-driven approaches, offering insights for optimizing future malaria elimination strategies in endemic regions.

The spatiotemporal ecology of Oropouche virus across Latin America: a multidisciplinary, laboratory-based, modelling study.

Vector control is a most widely used control method for induction motor (IM) of electric vehicle (EV). However, the EV battery voltage will rapidly drop to the under-voltage protection point, if the IM torque is output without any limited, especially when the EV battery level is insufficient. As a result, the under-voltage protection of IM driving system would be trigger in advance and the EV endurance will be seriously affected. To address this issue, a vector control method for EV under the constraint of battery output power is proposed. Based on the traditional vector control strategy, this method introduces the closed-loop control of battery output power and combines the power limit curve associated with motor speed and battery voltage to constrain the battery output power. The experimental results show that the proposed vector control method can effectively prevent premature under-voltage protection caused by excessive battery output current, and is helpful to improve the endurance of electric vehicles.