Conventional Control Scheme Research Articles

Efficiency improvement of dual active bridge converter using simple graphical optimization method

SummaryAiming to the problems of high circulating current and low efficiency caused by the traditional strategy of dual active bridge (DAB) converter under light load, in this paper, simple graphical optimization method (GSO) is proposed to improve the efficiency of dual active bridge converter. Through the contour plot of transmission power and current stress, optimal efficiency point can be obtained by tangent of contour map under condition of constant power, and heavily calculation or complex iterations of existing conventional control strategies are avoided. Moreover, analysis of proposed graphical current stress optimization is addressed, and controller design of proposed method is given and described. Efficiency comparison between traditional modulation strategy and GSO is given to show the advantage of proposed mothed. An experimental prototype is built to verify the analysis.

Sliding-Mode Control for Flight Stability of Quadrotor Drone Using Adaptive Super-Twisting Reaching Law

In this study, a sliding-mode controller is designed using an adaptive reaching law with a super-twisting algorithm. A dynamic model of a drone is designed with a quadrotor that has four motors and considers disturbances and model uncertainties. Given that the drone operates as an under-actuated system, its flight stability and maneuverability are influenced by the discontinuous signal produced by the reaching law of the sliding-mode control. Therefore, this study aims to improve the sliding-mode control and stability of drone flight using the proposed adaptive law, which is based on exponential properties. The discontinuous signal of a conventional strategy is overcome using the super-twisting algorithm, and the drone rapidly reaches equilibrium using the proposed adaptive law that utilizes the sliding surface value. The proposed control strategy covers a higher dimension than the conventional sliding-mode control strategy; the system stability is proven using the strict Lyapunov function. The reaching time estimation results are introduced and used to compare the respective reaching times of the control strategies. To verify the superior performance of the proposed control method, multiple experiments are conducted under various situations and realizations. The simulation results prove that the proposed control method achieved a superior rapid response, stable maneuvering, and robustness with shorter reaching time.

Operational control for the evolution of enthalpy in an SBR carrying out nitration of 4-chlorobenzotrifluoride and the thermal runway

BackgroundSemi Batch Reactors (SBR) are mostly used to carry out exothermic reactions (synthesis of 4-Chloro-3-Nitrobenzene-tri-fluoride) where a huge amount of heat is evolved. The excess enthalpy generated is removed by the cooling system which encloses the reactor. SBRs are subjected to several uncertainties, external and internal disturbances, and various other faults that occur during the processing of reactants. In the absence of a cooling system, the product starts to decompose yielding gaseous components which raise reactor temperature and pressure, finally leading to thermal runaway. From model-based effective prognosis, it was found that the reaction is very much sensitive to parameters like Damkoler number, activation energy, and the reaction-enthalpy-coefficient. MethodIn order to avoid hazardous situations, a suitable controller must be designed. In this work, a Fault Tolerant Sliding Mode Controller is found to be a promising tool to confront parametric uncertainties, disturbances, and faults. A Fault Tolerant Sliding Mode Observer is designed to observe the unmeasured disturbance and parameters. FindingsNitration of 4-Chlorobenzotrifluoride exhibits both interphase and intraphase mass-transfer phenomenon. The proposed control strategy was demonstrated on the SBR process and compared with the conventional control scheme to check feasibility and effectiveness like cooling rate, computation time, root mean square error, and mean square deviation. To guarantee convergence of the newly developed estimator and stability of the closed-loop controller, Lyapunov criteria are well investigated.

Novel adaptive power distribution master–slave control strategy for a biogas–solar–wind battery islanded microgrid based on a microturbine

As many countries increase their investment in the field of renewable energy, technological research and development has increased on establishing biogas–solar–wind battery islanded microgrids (BSWBIMs) based on biogas generation technology. In BSWBIMs, multiple distributed generators (DGs) are controlled to share either the lack or surplus of power throughout the microgrid. To improve the reliability and economic efficiency of the system, a reasonable control strategy is indispensable. In this paper, a novel adaptive power distribution master–slave (APD–MS) control strategy is proposed to maintain the system voltage and frequency within the Chinese national grid requirements. Moreover, compared with the conventional master-slave control strategy, the proposed control strategy can solve the problem of high dependence on the master DG. Notably, the proposed control strategy can improve the biogas utilization efficiency, system reliability and system stability. The theoretical analysis and software simulation based on BSWBIMs at low voltage levels demonstrate the effectiveness and superiority of the proposed APD–MS control strategy.

CAES-SC hybrid energy storage: Dynamic characteristics and control via discharge process

Energy storage is an effective means to increase renewable energy utilization. Compressed air energy storage (CAES) and super-capacitor (SC) hybrid energy storage system is one of the potential options for renewable energy microgrids due to its low-cost and flexible response capacity. In this paper, we thoroughly investigate the dynamical characteristics of the CAES system and the dynamical interactions between different subsystems in the CAES-SC hybrid energy storage via the discharge process. Dynamical models have been developed for the CAES discharge subsystem and the SC subsystem. Studies of the start-up process of CAES discharge subsystems have shown that the no-load start-up mode is more efficient than the variable-load start-up mode if the equivalent power generation for the same compressed air mass consumption is measured. In terms of the interaction characteristics between the CAES subsystem and the SC subsystem, the capacity demand of the SC subsystem in the no-load start-up mode is about 8 % higher than that in the variable-load start-up mode. An optimal control strategy based on the constraint of source-load dynamic balance has also been proposed to achieve power allocation in a CAES-SC hybrid energy storage system. Case studies show that the cycle efficiency of the hybrid energy storage system corresponding to the proposed optimal control strategy is 57.91 %, which is 1.84 % higher than that of the conventional control strategy. Moreover, compared with the conventional design that takes twice the maximum energy storage capacity demand during the starting process of the CAES discharge subsystem (about 1.6 MWh), a higher designed energy storage capacity (about 3 MWh) of the SC subsystem is required to satisfy the complete response to the source-load deviation under the proposed optimal control strategy. Moreover, for every 0.5 MWh increase in the energy storage capacity of the SC subsystem, the cycle efficiency of the hybrid energy storage system can be increased by approximately 0.3 % to 0.5 %.

Integrating Demand Response for Enhanced Load Frequency Control in Micro-Grids with Heating, Ventilation and Air-Conditioning Systems

Heating, ventilation and air-conditioning (HVAC) systems constitute the majority of the demands in modern power systems for aggregated buildings. However, HVAC integrated with renewable energy sources (RES) face notable issues, such as uneven demand–supply balance, frequency oscillation and significant drop in system inertia owing to sudden disturbances in nearby generation for a longer period. To overcome these challenges, load frequency control (LFC) is implemented to regulate the frequency, maintain zero steady-state error between the generation and demand, reduce frequency deviations and balance the active power flow with neighboring control areas at a specified value. In view of this, the present paper investigates LFC with a proposed centralized single control strategy for a micro-grid (µG) system consisting of RESs and critical load of a HVAC system. The proposed control strategy includes a newly developed cascaded two-degree-of-freedom (2-DOF) proportional integral (PI) and proportional derivative filter (PDF) controller optimized with a very recent meta-heuristic algorithm—a modified crow search algorithm (mCSA)—after experimenting with the number of performance indices (PICs). The superiority of both the proposed optimization algorithm and the proposed controller is arrived at after comparison with similar other algorithms and similar controllers, respectively. Compared to conventional control schemes, the proposed scheme significantly reduces the frequency deviations, improving by 27.22% from the initial value and reducing the performance index criteria (ƞISE) control error to 0.000057. Furthermore, the demand response (DR) is implemented by an energy storage device (ESD), which validates the suitability of the proposed control strategy for the µG system and helps overcome the challenges associated with variable RESs inputs and load demand. Additionally, the improved robustness of the proposed controller for this application is demonstrated through sensitivity analysis with ±20% μG coefficient variation.

An LSTM-PSO-Based Predictive Strategy and Its Application in the Main Steam Temperature Control in Power Plants

We proposed an LSTM-PSO predictive controller strategy to overcome the large delay and strong disturbance during main steam temperature control. First, the long short-term memory (LSTM) network model was developed to forecast the future trend of mainstream temperature, and the deviation between the model output and the expected value after feedback correction was minimized by the particle swarm optimization (PSO) algorithm. Then, the LSTM-PSO predictive controller was taken as the primary controller to maintain the steam temperature of the coal-fired boiler. A comparative simulation with conventional PID cascade control and dynamic matrix controller strategies was given, and the test calculations indicate that the adjustment time was shortened and the overshoot was reduced. Finally, the LSTM – PSO predictive control strategy was deployed into the actual boiler operation process of a 300 MW coal-fired power plant, within 0.5 °C steady-state error and 2 °C dynamic error.

Generalized Data-Driven Model-Free Predictive Control for Electrical Drive Systems

The performance of model predictive control has a strong correlation to the precision of the physical parameters of the plant, and these parameters are hard to determine since they are continuously changing during the operation process. To fully eliminate the influence of the physical parameters and enhance robustness, a model-free predictive control is proposed in this article to suit the electrical drive systems. The plant model is designed as several discrete-time transfer functions used to decouple the input and output signals and to describe their relationships, and the coefficients of these functions are online designed based on the recursive least square algorithm. An observer is designed to obtain accurately sampled current components considering the delays. The proposed method is applied to a permanent magnet synchronous motor speed control system as the stator current controller, and the simulation and experimental results show the advantages of the improved dynamics, stator current quality, and robustness compared with the conventional model-free predictive current control strategy.

An automated weed identification framework for sugarcane crop: A deep learning approach

Automatic weed identification using deep learning (DL) models will mark a revolution in developing site– specific artificial intelligence (AI) based herbicide sprayers which intend to maximize herbicide efficiency with reduced herbicide application in agriculture production systems and hence contribute to higher yields. Conventional weed control strategies pose challenges for integrating smart herbicide delivery and machinery systems. This deep learning approach significantly impacts developing a system for weed identification required in establishing successful real time precision weed management systems like smart spraying systems and AI based smart machinery. Minimal research has been done on automatic weed identification in sugarcane (Saccharum officinarum L.) cropping systems. This study analyzed the feasibility of a computer vision based DL approach for weed identification to achieve autonomous weed control. The image dataset containing 5660 augmented images was deployed to train and evaluate DL models with a spilt of 90% for training and the rest for validation. We trained the six DL models for identifying weeds in sugarcane crop using field imagery (5094 images), validated (566 images) and further evaluated their accuracy and F1 score performance. Model training was undertaken by varying the hyperparameters, such as mini batch size (16 and 32) and epoch (10, 20 and 30) at a learning rate of 0.001. DarkNet53 accomplished a high F1 score value (>99%) and outperformed other models (AlexNet, GoogLeNet, InceptionV3, ResNet50, and Xception) for the identification of weeds in actively growing sugarcane crop. Weeds can be identified with a higher level of confidence (>98%) with a minimum error rate (<1%) at the mini batch size of 16 and epochs 20. Post-training and validation, DarkNet53 was tested (stage I) with an independent 200-image dataset followed by stage II testing (30 images) and obtained 96.6% net accuracy compared with naked eye weed identification. Based on the high – level performance of DarkNet53, we conclude that DL based weed identification laid a potential future avenue with an effective decision system in the machine vision aspects of a precision herbicide applicator for weed control in sugarcane fields through a low cost camera integrated with a single board computer.

The role of state-of-charge management in optimal techno-economic battery energy storage sizing for off-grid residential photovoltaic systems

With increased photovoltaic (PV) penetration in residential areas, an off-grid PV system is a sustainable solution to meet the zero net emissions goal by 2050. However, an off-grid PV system has a significant technical issue: low power supply reliability. Typically, battery energy storage (BES) is necessary to overcome the technical issues associated with PV systems. Conventionally, BES is operated to maintain a power balance between generation and load. However, techno-economic feasibility is a crucial aspect of BES adoption. Although BES can effectively support off-grid PV systems, it may not be available when its state-of-charge (SoC) reaches the minimum allowable limit, creating power supply failures (outages). Therefore, this study proposes, for the first time, a BES power control strategy that collaborates with SoC management to improve the reliability of an off-grid residential PV-BES system. A simulation study is performed using a residential load profile from the urban area of Bangkok, Thailand, obtained from the Metropolitan Electricity Authority (MEA). A comparative study is conducted to compare the proposed strategy’s performance with that of the conventional control strategy. Following the comparative study, the role of SoC management in BES sizing is quantitatively illustrated. Moreover, the impacts of SoC management parameter setting (i.e., SoC restoration threshold and maximum SoC restoration power) are evaluated using techno-economic performance indicators. The simulation results reveal that BES power management with SoC management could reduce BES capacity and levelized cost of energy (LCOE) by 28% and 26.28% compared with the conventional strategy.

Comparative Performance Study of DVR Using Adaptive LMS Filtering-Based Algorithms

In the distribution system, voltage sags and swell have adverse effects on sensitive and critical loads. Dynamic Voltage Restorer (DVR) play a vital role to mitigate voltage sags and swell through its organized control when connected in series with the system. Various conventional control schemes have been reported for the control of DVR. The performance of DVR with these schemes has been successful in the mitigation of voltage sags/swell, but drawbacks, such as undershoot, and overshoot during sag dynamics are observed in the regulated load terminal voltage. These drawbacks may cause malfunctioning of the equipment connected and hence their elimination is indispensable. With this inspiration, a novel Adaline filter-based adaptive control scheme using learning-rate parameter-based Least-Mean-Square algorithm has been proposed. This scheme has the capability to overcome the drawbacks with the conventional control schemes thus improving the performance of DVR. In this paper, the performance of DVR using proposed algorithm is compared with that using synchronous reference frame (SRF)-based conventional control scheme. The performance is studied and validated through simulation results under different voltage sag and swell conditions in MATLAB software using Sim Power System toolboxes.

Circulating current control of MMC-fed induction motor drive under entire speed range operation

ABSTRACT A modular multilevel converter (MMC) is reflected to be an emerging multilevel topology for medium- to high-power induction motor drives due to its highly reliable structure, modularity and scalability. However, large circulating current produced in the phase arm of MMC during low-speed operation of induction motor drive is a major drawback of MMC, which leads to affect the size of a sub-module capacitor, inductor and switch rating. To mitigate the circulating current and improve the performance of MMC-fed induction motor drive, this paper proposed a new circulating current error compensator (CCEC) technique with reduced arm current peak. By proper control of instantaneous voltage between arms and DC link current, second-order fundamental frequency current harmonic from circulating current is eliminated which leads to a significant reduction in circulating current. A comparative evaluation between conventional control scheme and the proposed CCEC control scheme is presented. The performance and effectiveness of the proposed CCEC technique are validated using a MATLAB-based simulation. Scale-down laboratory experimental setup is also developed to check the effectiveness of the proposed CCEC control technique. Reduction in circulating current for entire speed range with improved DC link current profile is observed as compared to conventional circulating current suppression control.

Entomopathogenic fungal infection following immune gene silencing decreased behavioral and physiological fitness in Aedes aegypti mosquitoes

Entomopathogenic fungi are a promising category of biocontrol agents with mosquitocidal properties. Prior studies have proved their potential to reduce fecundity, human biting and vector competence, all of them together determine vectorial capacity of the mosquitoes. Unfortunately, conventional vector control strategies are inadequate with growing problem of insecticide resistance and environmental deterioration. Therefore, alternate vector control measures are immediately needed and to accomplish that, an improved understanding of behavioral and physiological defense mechanisms of the mosquitoes against fungal infection is essential. In this study, fitness was considered with respect to different behavioral (self-grooming and flight), physiological (antifungal activity and antimicrobial peptides) parameters and survival rates as compared to the control group. We found a significant upregulation in CLSP2, TEP22, Rel1 and Rel2 genes at multiple time periods of fungal infection, which indicates the successful fungal infection and activation of Toll and IMD pathways in mosquitoes. RNAi-mediated silencing of Rel1 and Rel2 genes (transcription factors of Toll and IMD pathways, respectively) significantly reduced the survival, self-grooming frequencies and durations, and flight locomotor activity among adult Ae. aegypti female mosquitoes. Moreover, Rel1 and Rel2 knockdown significantly decreased antifungal activity and antimicrobial peptides expression levels in target mosquitoes. These results indicate an overall decrease in fitness of the mosquitoes after fungal challenge following Rel1 and Rel2 silencing. These findings provide an improved understanding of behavioral and physiological responses in mosquitoes with altered immunity against entomopathogenic fungal infections which can guide us towards the development of novel biocontrol strategies against mosquitoes.

Third harmonic injection control for co‐phase traction power supply system using direct AC/AC full‐bridge modular multilevel converter

AbstractAs the full‐bridge modular multilevel converter (FB‐MMC) enables direct three‐phase AC voltage to single‐phase AC voltage conversion with good power quality and high voltage level, it is suitable for the co‐phase traction power supply system. However, voltage deviation occurs between arms when the direct AC/AC FB‐MMC operates in equal or similar frequency conditions. To solve this problem, a voltage balancing control based on third harmonic injection of co‐phase power supply system using direct AC/AC FB‐MMC is proposed. With the third harmonic injection, voltage balancing between arms is ensured and the capacity of FB‐MMC is enhanced. The third harmonic voltage is directly obtained from existing signals and the third harmonic current reference is produced by simple proportional‐integral (PI) controller. The effect of the third harmonic currents under different voltage ratios and phase difference is quantitatively analyzed, optimized range of voltage ratio and phase difference are obtained to minimize arm currents increment and reduce overrating of converter currents. In addition, a comparison between the proposed and the conventional control scheme is studied, showing the superior of proposed method in cost and performance. Finally, the effectiveness of the proposed scheme is verified by simulations and experiments.

Optimal Control Strategy for Ship Cabin’s Active Chilled Beam System Using Improved Multi-Objective Salp Swarm Algorithm

Heating, ventilation, and air conditioning (HVAC) systems are the second largest energy consumers on cruise ships after the propulsion system. As a kind of HVAC system, active chilled beam (ACB) systems have been widely used for cabin service due to their performance of energy efficiency and good thermal comfort. However, conventional control strategies for ships’ ACB systems are not intelligent enough and cannot balance energy consumption and cabin comfort during the voyages of ships. This study developed an optimal control strategy for cabins’ ACB systems. First, a simulation environment considering dynamic conditions is established in TRNSYS. Second, an artificial neural network model is utilized to predict the energy consumption of the ACB system, while the predicted percentage dissatisfied is adopted to represent cabins’ thermal discomfort. Third, an improved multi-objective salp swarm algorithm is proposed to dynamically minimize both energy consumption and thermal discomfort. A TRNSYS–MATLAB co-simulation testbed is established to simulate the cabins served by an ACB system on a small cruise ship navigating from Hong Kong to Shanghai for validation tests and a comparison study. Compared to the conventional strategies, the proposed strategy can achieve a maximum energy savings of 12% while maintaining a predicted mean vote index less than 0.5, meeting the comfort requirements set by ASHRAE.

Accurate Active and Reactive Power Sharing Based on a Modified Droop Control Method for Islanded Microgrids.

When multiple paralleled distributed generation (DG) units operate in an islanded microgrid, accurate power sharing of each DG unit cannot be achieved with a conventional droop control strategy due to mismatched feeder impedance. In this paper, a small AC signal (SACS)-injection-based modified droop control method is presented for accurate active and reactive power sharing among DG units. The proposed control method adjusts the voltage amplitude of each DG unit by injecting small AC signals to form a reactive power control loop. This strategy does not need communication links or to specifically obtain the physical parameter of the feeder impedance and only requires the local information. Moreover, the parameter design procedure and stability analysis are given full consideration. Finally, simulation and experimental results verify the effectiveness of the proposed control scheme, and accurate active and reactive power sharing can be achieved.

Dynamic modeling and flexible control of combined heat and power units integrated with thermal energy storage system

The high percentage of renewable energy connected to the grid requires high operational flexibility of combined heat and power (CHP) units. As two products of the CHP units, electricity and heat are coupled, making it challenging to achieve coordinated control. The thermal energy storage (TES) system can smooth out the fluctuations in heat load demand by storing or releasing heat, thus aiding in the decoupling and coordinated control of electricity and heat. In this paper, an optimized control strategy of CHP units integrated with the TES system is proposed to improve the units’ operating flexibility. First, according to the operating mechanism, the dynamic models of the CHP unit and the TES system are established. On this basis, considering the heat source steam active response based on the linear quadratic regulator and the heat-supply support of the TES system, the boiler-turbine-heating-storage coordinated control system of a CHP unit is designed. The simulation experiment is conducted in a 300 MW CHP unit. The results show that compared with the conventional control strategy, the MAE, MAPE, MSE, and RMSE indicators of the electrical power output are decreased by 45.88%, 45.28%, 71.95%, and 47.04%, respectively.

Applications to the dynamics of the suspension system of fast finite time stability in probability of p-norm stochastic nonlinear systems

This paper elucidates the finite-time stability in probability problem for a class of p-norm stochastic nonlinear systems. Among them, from the point of view of convergence speed, finite-time control does have optimal properties. Consequently, a fast finite-time criterion for deterministic systems is established. However, although there exist a significant amount of results on fast finite time stability, there is no article involves p-norm stochastic nonlinear systems case available so far. There are random disturbance terms that made the traditional fast finite-time control methods inapplicable. To overcome this problem, we establish a fast finite-time control criterion for stochastic systems by revamping the fast finite time method of deterministic systems. Then, with the aid of sign function and adding a power integrator technology, a non-Lipschitz continuous state feedback controller is established successfully. Next, the control strategy can effectively improve the control speed compared to the existing results in this paper. Finally, numerical and dynamic suspension simulations are offered to augment our theoretical analysis results, and comparison plots with conventional control strategies are shown.

Safe High Stiffness Impedance Control for Series Elastic Actuators using Collocated Position Feedback

Physical human-robot interaction applications requiring safety and compliance characteristics usually employ mechanical devices such as series elastic actuators (SEAs). So far, impedance control schemes for SEAs have been investigated to overcome the limited displayable stiffness at the end point, represented by the stiffness of a physical spring, in a passive manner. SEAs usually mount two encoders on both the motor and load sides for position measurements. However, in several applications, the installation of a load-side encoder is not feasible from both cost and manufacturing perspectives. In this scenario, a Kalman Filter based on micro electro mechanical system accelerometers has been proposed for estimating the external force and load side quantities, without the need for load-side encoders. Herein, a novel impedance control scheme leveraging this approach is proposed to effectively overcome the stiffness of the physical spring while maintaining passivity at the end point. Compared with conventional load-side encoder-based impedance control schemes, the proposed control scheme presents comparable performance, as demonstrated by simulation and experimental results.

Symmetry groupoids for pattern-selective feedback stabilization of the Chafee-Infante equation.

Reaction-diffusion equations are ubiquitous in various scientific domains and their patterns represent a fascinating area of investigation. However, many of these patterns are unstable and, therefore, challenging to observe. To overcome this limitation, we present new noninvasive feedback controls based on symmetry groupoids. As a concrete example, we employ these controls to selectively stabilize unstable equilibria of the Chafee-Infante equation under Dirichlet boundary conditions on the interval. Unlike conventional reflection-based control schemes, our approach incorporates additional symmetries that enable us to design new convolution controls for stabilization. By demonstrating the efficacy of our method, we provide a new tool for investigating and controlling systems with unstable patterns, with potential implications for a wide range of scientific disciplines.