64 research outputs found

    Robust Control of Series Active Power Filters for Power Quality Enhancement in Distribution Grids: Simulation and Experimental Validation

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    © 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.This paper presents a simulation study and an experimental implementation of a single-phase Series Active Power Filter (SAPF) for the mitigation of harmonics in the load voltage. The aim is to regulate the injection voltage of the SAPF to compensate the grid voltage via the injection transformer in addition to maintaining the load voltage stable. The control strategies investigated in this work include Backstepping Sliding Mode Control (BSMC) and a neuro-fuzzy controller based on ANFIS (Adaptive Neuro-Fuzzy Inference System) l. The proposed control strategies for the single-phase SAPF are initially evaluated in simulations under MATLAB/Simulink and then validated on a laboratory-scale hardware experimental set up consisting of a source and a single-phase SAPF. A comparative study of these controllers with respect to their performance and robustness in mitigating power quality against voltage disturbances and harmonics is presented. Both simulation and experimental results have demonstrated that ANFIS-based controller was able to achieve superior performance and a lower total harmonic distortion (THD) as compared to the other control methods.Peer reviewedFinal Accepted Versio

    Development and Implementation of Some Controllers for Performance Enhancement and Effective Utilization of Induction Motor Drive

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    The technological development in the field of power electronics and DSP technology is rapidly changing the aspect of drive technology. Implementations of advanced control strategies like field oriented control, linearization control, etc. to AC drives with variable voltage, and variable frequency source is possible because of the advent of high modulating frequency PWM inverters. The modeling complexity in the drive system and the subsequent requirement for modern control algorithms are being easily taken care by high computational power, low-cost DSP controllers. The present work is directed to study, design, development, and implementation of various controllers and their comparative evaluations to identify the proper controller for high-performance induction motor (IM) drives. The dynamic modeling for decoupling control of IM is developed by making the flux and torque decoupled. The simulation is carried out in the stationary reference frame with linearized control based on state-space linearization technique. Further, comprehensive and systematic design procedures are derived to tune the PI controllers for both electrical and mechanical subsystems. However, the PI-controller performance is not satisfactory under various disturbances and system uncertainties. Also, precise mathematical model, gain values, and continuous tuning are required for the controller design to obtain high performance. Thus, to overcome these drawbacks, an adapted control strategy based on Adaptive Neuro-Fuzzy Inference System (ANFIS) based controller is developed and implemented in real-time to validate different control strategies. The superiority of the proposed controller is analyzed and is contrasted with the conventional PI controller-based linearized IM drive. The simplified neuro-fuzzy control (NFC) integrates the concept of fuzzy logic and neural network structure like conventional NFC, but it has the advantages of simplicity and improved computational efficiency over conventional NFC as the single input introduced here is an error instead of two inputs error and change in error as in conventional NFC. This structure makes the proposed NFC robust and simple as compared to conventional NFC and thus, can be easily applied to real-time industrial applications. The proposed system incorporated with different control methods is also validated with extensive experimental results using DSP2812. The effectiveness of the proposed method using feedback linearization of IM drive is investigated in simulation as well as in experiment with different working modes. It is evident from the comparative results that the system performance is not deteriorated using proposed simplified NFC as compared to the conventional NFC, rather it shows superior performance over PI-controller-based drive. A hybrid fuel cell (FC) supply system to deliver the power demanded by the feedback linearization (FBL) based IM drive is designed and implemented. The modified simple hybrid neuro-fuzzy sliding-mode control (NFSMC) incorporated with the intuitive FBL substantially reduces torque chattering and improves speed response, giving optimal drive performance under system uncertainties and disturbances. This novel technique also has the benefit of reduced computational burden over conventional NFSMC and thus, suitable for real-time industrial applications. The parameters of the modified NFC is tuned by an adaptive mechanism based on sliding-mode control (SMC). A FC stack with a dc/dc boost converter is considered here as a separate external source during interruption of main supply for maintaining the supply to the motor drive control through the inverter, thereby reducing the burden and average rating of the inverter. A rechargeable battery used as an energy storage supplements the FC during different operating conditions of the drive system. The effectiveness of the proposed method using FC-based linearized IM drive is investigated in simulation, and the efficacy of the proposed controller is validated in real-time. It is evident from the results that the system provides optimal dynamic performance in terms of ripples, overshoot, and settling time responses and is robust in terms of parameters variation and external load

    Cyber-Resilient Control Structures in DC Microgrids with Cyber-Physical Threats

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    Design and implementation of a soft computing-based controller for a complex mechanical system

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    Soft-Computing basierende Regler beinhalten Algorithmen, die im Bereich des Maschinellen Lernens einzuordnen sind. Diese Regler sind in der Lage eine geeignete Steuerungsstrategie durch direkte Interaktion mit einer dynamischen Regelstrecke zu entwerfen. Sowohl klassische als auch moderne Reglerentwurfsmethoden hangen von der Genauigkeit des verwendeten dynamischen Systemmodells ab, was insbesondere bei steigender Komplexitat des Systems und auftretenden Modellunsicherheiten nicht mehr uneingeschrankt gewahrleistet werden kann. Die Ziele von Soft- Computing basierenden Reglern sind die Verbesserung der Gute des Regelverhaltens und eine geeignete Anpassung der Regler ohne eine mathematische Modellbildung auf Grundlage von physikalischen Gesetzen. Im Rahmen dieser Arbeit werden funf Algorithmen zur Modellbildung und Regelung dynamischer Systeme untersucht, welche auf dem Mehrschichten-Perzeptron-Netzwerk (Multi-Layer Perceptron network, MLP), auf der Methode der Support Vector Machine (SVM), der Gau-Prozesse, der radialen Basisfunktionen (Radial Basis Functions, RBF) sowie der Fuzzy-Inferenz-Systeme basieren. Im Anschluss an die Darstellung der zugrunde liegenden mathematischen Zusammenhange dieser Methoden sowie deren Hauptanwendungsfelder im Bereich der Modellbildung und Regelung dynamischer Systeme wird eine systematische Evaluierung der funf Methoden diskutiert. Anhand der Verwendung quantitativer Gutekennziern werden diese Methoden fur die Verwendung in der Modellbildung und Regelung dynamischer Systeme vergleichbar gegenubergestellt. Basierend auf den Ergebnissen der Evaluierung wird der SVM-basierte Algorithmus als Kernalgorithmus des Soft-Computing basierenden Reglers verwendet. Der vorgestellte Regler besteht aus zwei Hauptteilen, wobei der erste Teil aus einer Modellfunktion der dynamischen Regelstrecke und einem SVM-basierten Beobachter besteht, und der zweite Teil basierend auf dem Systemmodell eine geeignete Regelstrategie generiert. Die Verikation des SVM-basierten Regleralgorithmus erfolgt anhand eines FEM-Modells eines dynamischen elastischen Balken bzw. einseitig eingespannten elastischen Balkens. Dieses Modell kann z. B. als Ersatzmodell fur das mechanische Verhalten eines exiblen Roboterarms oder einer Flugzeugtrag ache verwendet werden. Der Hauptteil der Modellfunktion besteht aus einem automatischen Systemidentikationsalgorithmus, der auch die Integration eines systematischen Modellbildungsansatzes fur dynamische Systeme ermoglicht.Die Ergebnisse des SVM-basierten Beobachter zeigen ahnliches Verhalten zum Kalman- Bucy Beobachter. Auch die Sensitivitatsanalyse der Parameter zeigt eine bessere Gute der SVM-basierten Beobachter im Vergleich mit den Kalman-Bucy Beobachtern. Im Anschluss wird der SVM-basierte Regler zur Schwingungsregelung des Kragtragers verwendet. Hierbei werden vergleichbare Ergebnisse zum LQR-Regler erzielt. Eine experimentelle Validierung des SVM basierten Reglers erfolgt an Versuchsst anden eines elastischen Biegebalkens sowie eines invertierten Biegebalkens. Die Zustandsbeobachtung fuhrt zu vergleichbaren Ergebnissen verglichen mit einem Kalman-Bucy Beobachter. Auch die Modellbildung des elastischen Balkens fuhrt zu guten Ubereinstimmungen. Die Regelgute des Soft-Computing basierenden Reglers wurde am Versuchsstand des invertierten Biegebalkens experimentell erprobt. Es wird deutlich, dass Ergebnisse im Rahmen der erforderlichen Vorgaben erzielt werden konnen.The focus of this thesis is to obtain a soft computing-based controller for complex mechanical system. soft computing based controllers are based on machine learning algorithm that able to develop suitable control strategies by direct interaction with targeted dynamic systems. Classical and modern control design methods depend on the accuracy of the system dynamic model which cannot be achieved due to the dynamic system complexity and modeling uncertainties. A soft computing-based controller aims to improve the performance of the close loop system and to give the controller adaptation ability as well as to reduce the need for mathematical modeling based on physical laws. In this work ve dierent softcomputing algorithms used in the eld of modeling and controlling dynamic systems are investigated.These algorithms are Multi-Layer Perceptron(MLP) network, Support Vector Machine (SVM),Gaussian process, Radial Basis Function (RBF), and Fuzzy Inference System (FIS). The basic mathematical description of each algorithm is given. Additionally, the most recent applications in modeling and controlling of dynamic system are summarized. A systematic evaluation of the ve algorithms is proposed. The goal of the evaluation is to provide quantitative measure of the performance of soft computing algorithms when used in modeling and controlling a dynamic system. Based on the evaluation, the SVM algorithm is selected as the core learning algorithm for the soft computing based controller. The controller has two main units. The rst unit has two functions of modeling dynamic system and obtaining a SVM-based observer. The second unit is in charge of generating suitable control strategy based on the dynamic model obtained. The verication of the controller using SVM algorithm is done using an elastic cantilever beam modeled using Finite Element Method (FEM). An elastic cantilever beam can be considered as a representation of exible single-link manipulator or aircraft wing. In the core of the modeling unit, an automatic system identication algorithm which allows a systematic modeling approach of dynamic systems is implemented. The results show that the system dynamic model using SVM algorithm is accurate with respect to the FEM model. As for the SVM-based observer the results show that it has good estimation in comparison with to dierent Kalman-Bucy observers. The sensitivity to parameters variations analysis shows that the SVM-based observer has better performance than Kalman-Bucy observer. The SVM based controller is used to control the vibration of the cantilever beam; the results show that the model reference controller using SVM has a similar performance to LQR controller. The validation of the controller using SVM algorithm is carried out using the elastic cantilever beam test rig and the inverted cantilever beam test rig. The states estimation using SVM-based observer of the elastic cantilever beam test rig is successful and accurate compared to a Kalman-Bucy observer. Modeling of the elastic cantilever beam using the SVM algorithm shows good accuracy. The performance of controller is tested on the inverted cantilever beam test rig. The results show that required performance objective can be realized using this control strategy

    A Survey of Artificial Neural Network in Wind Energy Systems

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    Wind energy has become one of the most important forms of renewable energy. Wind energy conversion systems are more sophisticated and new approaches are required based on advance analytics. This paper presents an exhaustive review of artificial neural networks used in wind energy systems, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases. More than 85% of the 190 references employed in this paper have been published in the last 5 years. The methods are classified and analysed into four groups according to the application: forecasting and predictions; design optimization; fault detection and diagnosis; and optimal control. A statistical analysis of the current state and future trends in this field is carried out. An analysis of each application group about the strengths and weaknesses of each ANN structure is carried out. A quantitative analysis of the main references is carried out showing new statistical results of the current state and future trends of the topic. The paper describes the main challenges and technological gaps concerning the application of ANN to wind turbines, according to the literature review. An overall table is provided to summarize the most important references according to the application groups and case studies

    Modelling of an electro-hydraulic actutor using extended adaptive distance gap statistic approach

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    The existence of high degree of non-linearity in Electro-Hydraulic Actuator (EHA) system has imposed a challenging task in developing its model so that effective control algorithm can be proposed. In general, there are two modelling approaches available for EHA system, which are the dynamic equation modelling method and the system identification modelling method. Both approaches have disadvantages, where the dynamic equation modelling is hard to apply and some parameters are difficult to obtain, while the system identification method is less accurate when the system’s nature is complicated with wide variety of parameters, nonlinearity and uncertainties. This thesis presents a new modelling procedure of an EHA system by using fuzzy approach. Two sets of input variables are obtained, where the first set of variables are selected based on mathematical modelling of the EHA system. The reduction of input dimension is done by the Principal Component Analysis (PCA) method for the second set of input variables. A new gap statistic with a new within-cluster dispersion calculation is proposed by introducing an adaptive distance norm in distance calculation. The new gap statistic applies Gustafson Kessel (GK) clustering algorithm to obtain the optimal number of cluster of each input. GK clustering algorithm also provides the location and characteristic of every cluster detected. The information of input variables, number of clusters, cluster’s locations and characteristics, and fuzzy rules are used to generate initial Fuzzy Inference System (FIS) with Takagi-Sugeno type. The initial FIS is trained using Adaptive Network Fuzzy Inference System (ANFIS) hybrid training algorithm with an identification data set. The ANFIS EHA model and ANFIS PCA model obtained using proposed modelling procedure, have shown the ability to accurately estimate EHA system’s performance at 99.58% and 99.11% best fitting accuracy compared to conventional linear Autoregressive with External Input (ARX) model at 94.97%. The models validation result on different data sets also suggests high accuracy in ANFIS EHA and ANFIS PCA model compared to ARX model

    Development of Novel Compound Controllers to Reduce Chattering of Sliding Mode Control

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    The robotics and dynamic systems constantly encountered with disturbances such as micro electro mechanical systems (MEMS) gyroscope under disturbances result in mechanical coupling terms between two axes, friction forces in exoskeleton robot joints, and unmodelled dynamics of robot manipulator. Sliding mode control (SMC) is a robust controller. The main drawback of the sliding mode controller is that it produces high-frequency control signals, which leads to chattering. The research objective is to reduce chattering, improve robustness, and increase trajectory tracking of SMC. In this research, we developed controllers for three different dynamic systems: (i) MEMS, (ii) an Exoskeleton type robot, and (iii) a 2 DOF robot manipulator. We proposed three sliding mode control methods such as robust sliding mode control (RSMC), new sliding mode control (NSMC), and fractional sliding mode control (FSMC). These controllers were applied on MEMS gyroscope, Exoskeleton robot, and robot manipulator. The performance of the three proposed sliding mode controllers was compared with conventional sliding mode control (CSMC). The simulation results verified that FSMC exhibits better performance in chattering reduction, faster convergence, finite-time convergence, robustness, and trajectory tracking compared to RSMC, CSMC, and NSFC. Also, the tracking performance of NSMC was compared with CSMC experimentally, which demonstrated better performance of the NSMC controller

    An integrated intelligent nonlinear control method for a pneumatic artificial muscle

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    This paper proposes an advanced position-tracking control approach, referred to as an integrated intelligent nonlinear controller, for a pneumatic artificial muscle (PAM) system. Due to the existence of uncertain, unknown, and nonlinear terms in the system dynamics, it is difficult to derive an exact mathematical model with robust control performance. To overcome this problem, the main contributions of this paper are as follows. To actively represent the behavior of the PAM system using a grey-box model, neural networks are employed as equivalent internal dynamics of the system model and optimized online by a Lyapunov-based method. To realize the control objective by effectively compensating for the estimation error, an advanced robust controller is developed from the integration of the designed networks, and improvement of the sliding mode and backstepping techniques. The convergences of both the developed model and the closed-loop control system are guaranteed by Lyapunov functions. As a result, the overall control approach is capable of ensuring the system's performance with fast response, high accuracy, and robustness. Real-time experiments are carried out in a PAM system under different conditions to validate the effectiveness of the proposed method
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