Vector control of a grid-connected rectifier/inverter using an artificial neural network

Malaria vector control: Can repurposed agricultural insecticides offer solutions to resistance issues?

This paper presents a discrete-time adaptive current controller for grid-connected pulse width modulation voltage source converters with LCL filter. The main attribute of the proposed current controller is that, in steady state, the damping of the LCL resonance does not depend on the grid characteristic since the adaptive feedback gains ensure a predefined behavior for the closed-loop current control. An overview of model reference adaptive state feedback theory is presented aiming to give the reader the required background for the adaptive current control design. The digital implementation delay is included in the model, and the stability concerning the variation of the grid parameters is analyzed in detail. Furthermore, current distortions due to the grid background voltage are rejected without using the conventional stationary resonant controllers or the synchronous proportional-plus-integral controllers. Simulation and experimental results are presented to validate the analysis and to demonstrate the good performance of the proposed controller for grid-connected converters subjected to large grid impedance variation and grid voltage disturbances.

This paper analyzes optimal control of a grid-connected converter (GCC) based on the adaptive critic designs (ACDs), especially on heuristic dynamic programming (HDP). Instead of using a trained model neural network to identify the dynamics of the plant, the paper uses exact GCC plant mathematical model to reflect the system dynamics accurately. Thus, the HDP for GCC only contains a critic neural network and an action neural network, which simplifies the control design. The training cycles of the critic and action networks in our HDP design are combined into one training cycle, which reduces the calculation time and makes it more suitable to online training. Results show that the RNN controller based on the designed HDP exhibits good tracking ability. The weights of the critic and action networks are adjusted online to adapt the changing GCC system automatically.

This paper presents the idea of vector control for permanent magnet synchronous motor (PMSM) based on chaos theorem, using chaos controller. PMSM will demonstrate chaotic phenomena when its parameters fall into a certain area. To achieve this aim, the sub-system of controller has been designed by considering block diagram structure of vector control for PMSM and by applying the setting of Lyapunov exponents method. Also, asymptotical stability of closed loop system with given controller is shown, using the direct Lyapunov method. The performance of designed controller in chaotic mode is compared with conventional vector control methods. Also, the normal mode for PMSM is considered and the performance of controller is compared. Simulation results indicate that not only does this controller eliminate the chaos in chaotic mode and have good performance but also is able to control the system in normal mode by using almost the smaller control signal effort.

Novel molecular approaches to combat vectors and vector-borne viruses: Special focus on RNA interference (RNAi) mechanisms

An improved neural networks-based vector control approach for permanent magnet linear synchronous motor

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.

The Sixth International Symposium on Neural Networks (ISNN 2009)

Vector control is an effective technique for controlling variable speed drives using brushless DC motors. Conventional vector controllers, however, suffer from electrical machine parameter variations because these controllers depend on the parameters. This paper presents the vector control of a brushless DC motor using a neural network. In the proposed method, a neural network is employed as an online estimator of the nonlinear dynamic equations of a brushless DC motor. The neural network based vector controller has the advantage of robustness against machine parameter variations as compared with the conventional vector controller. Computer simulation results verify the useful of the proposed method.

This paper presents an investigation of an artificial neural network (ANN) to predict the flutter characteristics of a simple wing that under went multiple weight variation changes. The weight variations were used to simulate the addition of non-structural weight or structural repair weight. The test case wing had 24 designated repair areas where repair weights were added as lumped masses. Weight and location of the repairs were randomly generated to increase the sample size and help the generalization of the ANN. The ANN was evaluated for both the flutter speed and flutter frequency for multiple levels of structural damping. The number of test cases required to train the neural network was also evaluated for both the error between the input and output results as well as the generalization of the neural networks.

The three-phase grid-connected converter control system and the grid impedance are coupled with each other under weak grid conditions, which brings in negative influence on the system. In this paper, the impedance model of a three-phase grid-connected converter including current loop, PLL and DC voltage loop is established in d-q frame. The influence of PLL and DC voltage loop proportional coefficient on system stability is analyzed. According to the transfer relationship of each variable in the impedance model, the PCC voltage perturbation paths to the PLL and DC voltage loop are analyzed respectively. On the basis of voltage perturbation paths, a compensation control strategy is proposed. The impedance analysis results show that the proposed method can effectively improve the system stability under weak. The experimental results verify the correctness of the theoretical analysis and the effectiveness of the proposed method.

For better performance, different current control schemes are used in LCL-type grid-connected converters. However, these schemes are mostly used in three-phase grid-connected converters and multiple current sensors are needed. To address these issues, a current decoupling control scheme for single-phase grid-connected converters in rotating reference frame is proposed in the paper. Because an extended state observer is adopted in the control scheme, only one current sensor is needed and the complicated coupling relationships between the $d$ axis components and $q$ axis components can be eliminated completely. Moreover, to improve the dynamic and steady-state performance of converters, a state feedback closed-loop system structure and nonlinear controllers are adopted. The current decoupling control scheme is validated in an LCL-type single-phase grid-connected converter. The experimental results show that a fast-dynamic response under load change and voltage sag can be achieved besides preferable static performances with the proposed control scheme.

The present paper investigates an advanced control system of the wind energy conversion using an Artificial Neural Network (ANN) controller, which is developed in two modes: the offline mode, required for testing different sets of neural network parameters to find the optimal neural network controller (structure, activation function, and training algorithm), and the online mode where the optimal ANN controller is used in the five-phase PMSG based wind turbine system. The required data to generate the ANN model is obtained from a conventional PI controller. The controller inputs are the error between reference and actual speed (e), and the error change (de = e(k)-e(k-1)). As for the ANN output, it is the decreasing or increasing electromagnetic torque (dTe*). The studied neural network controller has been tested and validated using different wind speed values. Results and analysis are presented, and the contribution has been demonstrated.

A microgrid consists of a variety of inverter-interfaced distributed energy resources (DERs). A key issue is how to control DERs within the microgrid and how to connect them to or disconnect them from the microgrid quickly. This paper presents a strategy for controlling inverter-interfaced DERs within a microgrid using an artificial neural network, which implements a dynamic programming algorithm and is trained with a new Levenberg-Marquardt backpropagation algorithm. Compared to conventional control methods, our neural network controller exhibits fast response time, low overshoot, and, in general, the best performance. In particular, the neural network controller can quickly connect or disconnect inverter-interfaced DERs without the need for a synchronization controller, efficiently track fast-changing reference commands, tolerate system disturbances, and satisfy control requirements at grid-connected mode, islanding mode, and their transition.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.