Robust Back-stepping Based Higher Order Sliding Mode Control of Non-Inverted Buck-Boost Converter for a Photovoltaic System

Photovoltaic (PV) system generates renewable energy from sunlight, which has low efficiency due to the variance in nature of temperature and irradiance in a fast changing environment condition. Different researchers have proposed different maximum power point tracking MPPT techniques to improve the efficiency. However, still there are many open issues. Thus, to address this, a non-linear back-stepping–based higher order sliding mode controller (BHOSMC) is proposed to harvest maximum power from PV system. The PV module and load is interfaced by a non-inverted buck-boost converter (NIBBC). A linear interpolation method is used for voltage generation and Lyapunov stability is used to verify the control system equation. MATLAB/Simulink software is used for testing the proposed controller performance. The experimental result verified that the proposed BHOSMC is robust, accurate and fast tracking, faultless and less chattering as compared to perturb and observe (P&O), back-stepping control (BSC) and back-stepping-based sliding mode control under rapidly varying meteorological condition.

A Bidirectional Buck-Boost Converter-Based Switching Ripple Communication Strategy for Intelligent Street Lighting Systems

The light-emitting diode (LED) is an essential component of intelligent street lighting (ISL) systems. An efficient ISL system can not only reduce power consumption by planning LED illuminating time but also reduce maintenance costs through a high degree of automation. In this paper, a buck-boost converter is used to realise composite transmission of power and signals for an ISL system. The power is modulated by the pulse width modulation (PWM) approach, and the switching ripple generated in the PWM process is utilised as the carrier of the signals transmitted between the remote-control centre and the slave nodes. Moreover, the proposed model involves a ‘request to send (RTS)/confirm to send (CTS)’ mechanism to avoid signal conflicts. Compared with the conventional power line communication (PLC) approach, the proposed transmission scheme has the advantages of simple circuit structure and simple system wiring. Additionally, a simulation model built in MATLAB/Simulink proves the designed transmission method has strong anti-noise ability.

Enhanced Reliability for Winding Machine via New Fault Tolerant Control Based on RST-Backstepping Controller

Due to the external disturbances, model uncertainties, strong coupling, and occurred faults, the winding machine presents a great control challenge. In order to deal with these problems, this paper presents the formulation of a novel scheme of fault tolerant control (FTC) for three-motor web-winding systems; it is concerned with the nonlinear robust backstepping control based on the combination of RST and backstepping controllers where the process is modelled by a nonlinear model. The main contribution of the paper is that the approach developed here summarises the performance of RST and backstepping controllers in order to design a robust controller capable of eliminating external disturbances and sensor faults affecting the system. The stability of the whole system is proven using the Lyapunov theory. Finally, analysis in comparison with the conventional backstepping controller and simulations in the MATLAB environment are accomplished to confirm the efficiency of the proposed method.

Possibilities for Energy Saving Predictions in Elevators

Today, with the longing for smart and sustainable transportation, the elevator industry has undergone major metamorphism in the field of control algorithm, electric drive, and the motor. Amongst these, regenerative drive (RD) plays a pivotal role in making elevator technology more energy efficient. Rather than wasting the recovery energy from the machine as heat, RD recovers it as green energy. Conventional direct current (DC) motors ruled the elevator industry for many years and were adopted as standard type of elevator motors. But with the advancement in electric drive technology, alternating current (AC) motors, especially induction motors, flourished in the later part. Recently with the introduction of Permanent Magnet Synchronous Motors (PMSM) technology, the elevator revolution began in terms of power quality, ride quality, and green energy. Likewise, contrasted with different types of vertical transportation machines, PMSMs have better powerful execution, compact size, and higher system-level efficiency. Recently, with the rapid improvement in intensity hardware, utilization of rare earth magnetic materials, and indubitably advanced research, PMSM has rapidly changed systems globally. PMSM is a multivariable, nonlinear, and high-coupling framework. The torque and stator current present a unique capacity connection. Attractive fields can be decoupled to gain decent power outcomes. With the presentation of regenerative PMSM, electrical drives coupled to system integrated frameworks for recovery energy has enhanced savings in power consumptions.

The Effect of Different Decision-Making Methods on Multi-Objective Optimisation of Predictive Torque Control Strategy

Today, a clear trend in electrification process has emerged in all areas to cope with carbon emissions. For this purpose, the widespread use of electric cars and wind energy conversion systems has increased the attention and importance of electric machines. To overcome limitations in mature control techniques, model predictive control (MPC) strategies have been proposed. Of these strategies, predictive torque control (PTC) has been well accepted in the control of electric machines. However, it suffers from the selection of weighting factors in the cost function. In this paper, the weighting factor associated with the flux error term is optimised by the non-dominated sorting genetic algorithm (NSGA-II) algorithm through torque and flux errors. The NSGA-II algorithm generates a set of optimal solutions called Pareto front solutions, and a possible solution must be selected from among the Pareto front solutions for use in the PTC strategy. Unlike the current literature, three decision-making methods are applied to the Pareto front solutions and the weighting factors selected by each method are tested under different operating conditions in terms of torque ripples, flux ripples, cur-rent harmonics and average switching frequencies. Finally, a decision-making method is recommended.

Control Strategy of Parallel Systems with Efficiency Optimisation in Switched Reluctance Generators

To solve motor heating and life shortening of parallel switched reluctance generator (SRG) induced by uneven output currents due to different external characteristics, we generally adopt current sharing control (CSC) to make each parallel generator undertake large load currents on average to improve the reliability of parallel power generation system. However, the method usually causes additional loss of power because it does not consider the efficiency characteristics of each parallel generator. Therefore, with the efficiency expression for the parallel system of SRG established and analysed, the control strategy based on differential evolution (DE) algorithm is proposed as a mechanism by which to enhance generating capacity and reliability of multi-machine power generation from the perspective of efficiency optimisation. We re-adjust the reference current of each parallel generator to transform the working point of each generator and implement the efficiency optimisation of parallel system. The performance of the proposed control method is evaluated in detail by the simulation and experiment, and comparison with traditional CSC is carried out as well.

Control of a Four-wire Hybrid Prosumer Converter for Balancing Utility Grids

The paper presents the project of the power electronic AC/DC converter operating under a multiresonant control algorithm for prosumer applications. This design allows independent control of active and reactive power for any or each phase. Both the converter and its algorithm are based on a three-phase converter of four-wire topology (AC/DC 3p-4w) with a DC bus, which couple the converter to a renewable energy source and energy storage. Further, model and simulation tests were carried out in the Matlab-SIMULINK programming environment. The results obtained indicate that operation with deep unbalances and powers of opposite signs in individual phases results in current variations (oscillations) in the DC line, which is a significant limitation, and it can be concluded that the level of asymmetry should be limited to the level acceptable to the energy storage device.

Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

Due to their many advantages, permanent magnet synchronous motors (PMSMs) are increasingly used in not only industrial drive systems but also electric and hybrid vehicle drives, aviation and other applications. Unfortunately, PMSMs are not free from damage that occurs during their operation. It is assumed that about 40% of the damage that occurs is related to rolling bearing damage. This article focuses on the use of Kohonen neural network (KNN) for rolling bearing damage detection in a PMSM drive system. The symptoms from the fast Fourier transform (FFT) and Envelope (ENV) Analysis of the mechanical vibration acceleration signal were analysed. The signal ENV was obtained by applying the Hilbert transform (HT). Two neural network functions are discussed: a detector and a classifier. The detector detected the damage and the classifier determined the type of damage to the rolling bearing (undamaged bearing, damaged rolling element, outer or inner race). The effectiveness of the analysed networks from the point of view of the applied signal processing method, map size, type of neighbourhood radius, distance function and the influence of input data normalisation are presented. The results are presented in the form of a confusion matrix, together with 2D and 3D maps of active neurons.

Sliding Mode Control of Hybrid Renewable Energy System Operating in Grid Connected and Stand-Alone Mode

This paper studies innovative application of sliding mode control (SMC) for a Hybrid Renewable Energy System (HRES) in grid-connected and autonomous modes of operation. The considered HRES includes a photovoltaic (PV), wind turbine (WT) based on a Permanent Magnet Synchronous Generator (PMSG). The PV generator is coupled to the common DC bus via a DC/DC converter. The latter is controlled by an MPPT algorithm based on the Adaptive Perturbation and Observation Algorithm Method (APOAM) to search the optimum working of this source. A SMC is utilized to manage the PV voltage to achieve the Maximum Power Point (MPP) by altering the obligation duty cycle. The battery interfaced by a bidirectional buck-boost DC/DC converter can be charged or discharged depending on the production situation. On the one hand, the wind turbine conversion chain is equipped with a PMSG and a rectifier controlled to regulate the operating point of the wind turbine to its optimum value. During a Stand-Alone Mode (SAM) operation, the Voltage Source Converter (VSC) was used for controlling the output voltage in terms of amplitude and frequency delivered to the AC load. However, in Grid-Connected Mode (GCM) operation, the VSC was adapted to control the electrical parameters of the grid. To better appreciate the advantages of the proposed SMC approach, we have proposed a series of comparative tests with the conventional PI control in the operating modes GC and SA and under different scenarios. The proposed control strategy has undeniable advantages in terms of control performance and very low total harmonic distortion THD value compared with the conventional PI control. Finally, It is concluded that the proposed approach improves the quality and provides a stable operation of the HRES.

Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor

This paper focuses on designing a gain-scheduled (G-S) state feedback controller (SFC) for synchronous reluctance motor (SynRM) speed control with non-linear inductance characteristics. The augmented model of the drive with additional state variables is introduced to assure precise control of selected state variables (i.e. angular speed and d-axis current). Optimal, non-constant coefficients of the controller are calculated using a linear-quadratic optimisation method. Non-constant coefficients are approximated using an artificial neural network (ANN) to assure superior accuracy and relatively low usage of resources during implementation. To the best of our knowledge, this is the first time when ANN-based gain-scheduled state feedback controller (G-S SFC) is applied for speed control of SynRM. Based on numerous simulation tests, including a comparison with a signum-based SFC, it is shown that the proposed solution assures good dynamical behaviour of SynRM drive and robustness against q-axis inductance, the moment of inertia and viscous friction fluctuations.