Photovoltaic Emulation System and Maximum Power Point Tracking Algorithm Under Partial Shading Conditions

In this thesis, a novel photovoltaic (PV) emulator and the state-of-art learning–based real-time hybrid maximum power point tracking (MPPT) algorithms have been presented. Real-time research on PV systems is a challenging task because it requires a precise PV emulator that can faithfully reproduce the nonlinear properties of a PV array. The prime objective of the constructed emulator based on integration of unilluminated solar panels with external current sources is to overcome the constraints such as the need for wide surrounding space, high installation cost, and lack of control over the environmental conditions. In addition, the proposed PV emulator is able to simulate the electrical characteristics of the PV system under uniform irradiation as well as partially shading conditions (PSC). Moreover, the application of MPPT technology in PV systems under PSC conditions is challenging. Under complex environmental conditions, the power-voltage (P-V) characteristic curve of a PV system is likely to contain both local global maximum power points (LMPPs) and global maximum power points (GMPP). The MPPT algorithm applied to a PV system should have minimal steady-state oscillations to reduce power losses while accurately searching for the GMPP. The proposed MPPT algorithms resolved the drawbacks of the conventional MPPT method that have poor transient response, high continuous steady-state oscillation, and inefficient tracking performance of maximum power point voltage in the presence of partial shading. The intended algorithms have been verified using MATLAB/Simulink and the proposed PV emulator by applying comparative analysis with the traditional MPPT algorithms. In addition, the performance of the proposed MPPT algorithms and control scheme is validated experimentally with the implementation of MATLAB/Simulink/Stateflow on dSPACE Real-Time-Interface (RTI) 1007 processor board and DS2004 A/D and CP4002 Digital I/O boards. The results indicate that the algorithm is effective in reducing power losses and faster in tracking the speed of the maximum power point with less oscillation under partial shading conditions. In addition, excellent dynamic characteristics of the proposed emulator have been proven to be an ideal tool for testing PV inverters and various maximum power point tracking (MPPT) algorithms for commercial applications and university studies

Design and Implementation of Control Techniques of Power Electronic Interfaces for Photovoltaic Power Systems

The aim of this thesis is to scrutinize and develop four state-of-the-art power electronics converter control techniques utilized in various photovoltaic (PV) power conversion schemes accounting for maximum power extraction and efficiency. First, Cascade Proportional and Integral (PI) Controller-Based Robust Model Reference Adaptive Control (MRAC) of a DC-DC boost converter has been designed and investigated. Non-minimum phase behaviour of the boost converter due to right half plane zero constitutes a challenge and its non-linear dynamics complicate the control process while operating in continuous conduction mode (CCM). The proposed control scheme efficiently resolved complications and challenges by using features of cascade PI control loop in combination with properties of MRAC. The accuracy of the proposed control system’s ability to track the desired signals and regulate the plant process variables in the most beneficial and optimised way without delay and overshoot is verified. The experimental results and analysis reveal that the proposed control strategy enhanced the tracking speed two times with considerably improved disturbance rejection. Second, (P)roportional Gain (R)esonant and Gain Scheduled (P)roportional (PR-P) Controller has been designed and investigated. The aim of this controller is to create a variable perturbation size real-time adaptive perturb and observe (P&O) maximum power point tracking (MPPT) algorithm. The proposed control scheme resolved the drawbacks of conventional P&O MPPT method associated with the use of constant perturbation size that leads to a poor transient response and high continuous steady-state oscillations. The prime objective of using the PR-P controller is to utilize inherited properties of the signal produced by the controller’s resonant path and integrate it to update best estimated perturbation that represents the working principle of extremum seeking control (ESC) to use in a P&O algorithm that characterizes the overall system learning-based real time adaptive (RTA). Additionally, utilization of internal dynamics of the PR-P controller overcome the challenges namely, complexity, computational burden, implantation cost and slow tracking performance in association with commonly used soft computing intelligent systems and adaptive control strategies. The experimental results and analysis reveal that the proposed control strategy enhanced the tracking speed five times with reduced steady-state oscillations around maximum power point (MPP) and more than 99% energy extracting efficiency.Third, the interleaved buck converter based photovoltaic (PV) emulator current control has been investigated. A proportional-resonant-proportional (PR-P) controller is designed to resolve the drawbacks of conventional PI controllers in terms of phase management which means balancing currents evenly between active phases to avoid thermally stressing and provide optimal ripple cancellation in the presence of parameter uncertainties. The proposed controller shows superior performance in terms of 10 times faster-converging transient response, zero steady-state error with significant reduction in current ripple. Equal load sharing that constitutes the primary concern in multi-phase converters has been achieved with the proposed controller. Implementing of robust control theory involving comprehensive time and frequency domain analysis reveals 13% improvement in the robust stability margin and 12-degree bigger phase toleration with the PR-P controller. Fourth, a symmetrical pole placement Method-based Unity Proportional Gain Resonant and Gain Scheduled Proportional (PR-P) Controller has been designed and investigated. The proposed PR-P controller resolved the issues associated with the use of the PI controller which are tracking repeating control input signal with zero steady-state and mitigating the 3rd order harmonic component injected into the grid for single-phase PV systems. Additionally, the PR-P controller has overcome the drawbacks of frequency detuning in the grid and increase in the magnitude of odd number harmonics in the system that constitute the common concerns in the implementation of conventional PR controller. Moreover, the unprecedented design process based on changing notch filter dynamics with symmetrical pole placement around resonant frequency overcomes the limitations that are essentially complexity and dependency on the precisely modelled system. The verification and validation process of the proposed control schemes has been conducted using MATLAB/Simulink and implementing MATLAB/Simulink/State flow on dSPACE Real-time-interface (RTI) 1007 processor, DS2004 High-Speed A/D and CP4002 Timing and Digital I/O boards

Design of optimal rule-based controller for plug-in series hybrid electric vehicle

International audienceEnergy consumption of Hybrid Electric Vehicles (HEV) strongly depends on the adopted energy management strategy (EMS). Rule-Based (RB) controllers are the most commonly used for their ability of integration in real-time applications. Unlike global optimization routines, RB controllers do not ensure optimal energy savings. This study presents a methodology to design a close-to-optimal RB controller derived from global optimization strategies. First, dynamic programming (DP) optimization is used to derive the optimal behaviour of the powertrain components on the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), and then, the resulting performance of the powertrain components is used to design an optimized RB energy management strategy. Furthermore, the strategy is developed to cope with the variations in trip length and traffic conditions. The plug-in series hybrid electric vehicle is modelled using the energetic macroscopic representation (EMR). Results show that the proposed optimal RB controller is only consuming 1-2% more fuel compared to DP controllers and is resulting in a 13-16% less fuel consumption compared to basic RB controllers

Application of fuzzy-sliding mode control and electronic load emulation to the robust control of motor drives

This thesis is concerned with the experimental investigation of robust speed control strategies for the industrial motor drive systems. The first objective of the thesis is to implement a high performance programmable dynamometer which can provide desired linear and non-linear mechanical loads for the experimental validation of the robust control methods. The discrete time implementation of the conventional dynamometer control strategy (the inverse model approach) is analysed and it is shown that this method suffers from the stability and noise problems. A new dynamometer control strategy, based on speed tracking and torque feedforward compensation, is developed and successfully implemented in the experimental system. The emulation is placed in a closed loop speed control system and the experimental results are compared with the corresponding ideal simulated results for the validation of the dynamometer control strategy. The comparisons show excellent agreement for a variety of linear and nonlinear mechanical load models and such a high performance experimental load emulation results are reported for the first time in research literature. The second objective of the project is to investigate the Fuzzy Logic Control (FLC) and the Sliding Mode Control (SMC) approaches in order to develop a simple,• algorithmic and practical robust control design procedure for industrial speed drive control systems. The Reaching Law Control (RLC) method, which is an approach to SMC design, and the FLC are used together in order to develop a practical robust speed control strategy. The robustness of the proposed control approach is tested for a variety of linear and non-linear mechanical loads provided by the dynamometer. Using the new robust control method, good output responses are obtained for large parameter variations and external disturbances

Long Short-Term Memory and Fuzzy Logic for Anomaly Detection and Mitigation in Software-Defined Network Environment

[EN] Computer networks become complex and dynamic structures. As a result of this fact, the configuration and the managing of this whole structure is a challenging activity. Software-Defined Networks(SDN) is a new network paradigm that, through an abstraction of network plans, seeks to separate the control plane and data plane, and tends as an objective to overcome the limitations in terms of network infrastructure configuration. As in the traditional network environment, the SDN environment is also liable to security vulnerabilities. This work presents a system of detection and mitigation of Distributed Denial of Service (DDoS) attacks and Portscan attacks in SDN environments (LSTM-FUZZY). The LSTM-FUZZY system presented in this work has three distinct phases: characterization, anomaly detection, and mitigation. The system was tested in two scenarios. In the first scenario, we applied IP flows collected from the SDN Floodlight controllers through emulation on Mininet. On the other hand, in the second scenario, the CICDDoS 2019 dataset was applied. The results gained show that the efficiency of the system to assist in network management, detect and mitigate the occurrence of the attacks.This work was supported in part by the National Council for Scientific and Technological Development (CNPq) of Brazil under Project 310668/2019-0, in part by the SETI/Fundacao Araucaria due to the concession of scholarships, and in part by the Ministerio de Economia y Competitividad through the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento, under Grant TIN2017-84802-C2-1-P.Novaes, MP.; Carvalho, LF.; Lloret, J.; Lemes Proença, M. (2020). Long Short-Term Memory and Fuzzy Logic for Anomaly Detection and Mitigation in Software-Defined Network Environment. IEEE Access. 8(1):83765-83781. https://doi.org/10.1109/ACCESS.2020.2992044S83765837818

Fast‐converging robust PR‐P controller designed by using symmetrical pole placement method for current control of interleaved buck converter‐based PV emulator

In this study, the interleaved buck converter-based photovoltaic (PV) emulator current control is presented. A proportional-resonant-proportional (PR-P) controller is designed to resolve the drawbacks of conventional PI controllers in terms of phase management, which means balancing currents evenly between active phases to avoid thermally stressing and provide optimal ripple cancelation in the presence of parameter uncertainties. The resonant path of the controller (PR) with a constant proportional unity gain is designed considering the changing dynamics of a notch filter by pole placement method (adding mutually complementary poles to the notch transfer function) at PWM switching frequency. The proportional gain path (P) of the controller is used to determine the compatibility of the controller with parameter uncertainty of the phases and designed by utilizing loop-shaping method. The proposed controller shows superior performance in terms of 10 times faster-converging transient response, zero steady-state error with significant reduction in current ripple. Equal load sharing that constitutes the primary concern in multiphase converters is achieved with the proposed controller. Implementing of robust control theory involving comprehensive time and frequency domain analysis reveals 13% improvement in the robust stability margin and 12-degree bigger phase toleration with the PR-P controller. In addition to these, the proposed unconventional design process of the controller reduces the computational complexity and provides cost-effectiveness and simple implementation. Moreover, implementing of auxiliary resistor-capacitor (RC) circuits parallel with the inductors to sense the current in each phase removes the need for current measurement sensors that contribute to overall cost of the system

ANFIS control of a shunt active filter based with a five-level NPC inverter to improve power quality

© 2021 The Author(s). This is an open access article under the CC BY-SA license (https://creativecommons.org/licenses/by-sa/4.0/).This paper addresses the problem of power quality, and the degradation of the current waveform in the distribution network which results directly from the proliferation of the nonlinear loads. We propose to use a five-level Neutral Point Clamped (NPC) inverter topology for the implementation of the shunt active filter (SAPF). The aim of the SAPF is to inject harmonic currents in phase opposition at the connection point. The identification of harmonics is based on the pq method. A neuro-fuzzy controller based on ANFIS (Adaptive Neuro Fuzzy Inference System) is designed for the SAPF. The simulation study is carried out using MATLAB/Simulink and the results show a significant improvement in the quality of energy and a reduction in Total Harmonic Distortion (THD) in accordance with IEC standard, IEEE-519, IEC 61000, EN 50160.Peer reviewe

Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

sharedcircuitmodels is presented in this work. The sharedcircuitsmodelapproach of sociocognitivecapacities recently proposed by Hurley in The sharedcircuitsmodel (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences 31(1) (2008) 1–22 is enriched and improved in this work. A five-layer computational architecture for designing artificialcognitivecontrolsystems is proposed on the basis of a modified sharedcircuitsmodel for emulating sociocognitive experiences such as imitation, deliberation, and mindreading. In order to show the enormous potential of this approach, a simplified implementation is applied to a case study. An artificialcognitivecontrolsystem is applied for controlling force in a manufacturing process that demonstrates the suitability of the suggested approac