Roach Infestation Optimization MPPT Algorithm of PV Systems for Adaptive to Fast-Changing Irradiation and Partial Shading Conditions

Of all the renewable energy sources, solar photovoltaic (PV) power is considered to be a popular source owing to several advantages such as its free availability, absence of rotating parts, integration to building such as roof tops and less maintenance cost. The nonlinear current–voltage (I–V) characteristics and power generated from a PV array primarily depends on solar insolation/irradiation and panel temperature. The power output depends on the accuracy with which the nonlinear power–voltage (P–V) characteristics curve is traced by the maximum power point tracking (MPPT) controller. A DC-DC converter is commonly used in PV systems as an interface between the PV panel and the load, allowing the follow-up of the maximum power point (MPP). The objective of an efficient MPPT controller is to meet the following characteristics such as accuracy, robustness and faster tracking speed under partial shading conditions (PSCs) and climatic variations. To realize these objectives, numerous traditional techniques to artificial intelligence and bio-inspired techniques/algorithms have been recommended. Each technique has its own advantage and disadvantage. In view of that, in this thesis, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). In addition, the mathematical formulations and operation of the boost converter is investigated. To validate the effectiveness of the proposed RIO MPPT algorithm, MATLAB/Simulink simulations are carried out under varying environmental conditions, for example step changes in solar irradiance, and partial shading of the PV array. The obtained results are examined and compared with the particle swam optimization (PSO). The results demonstrated that the RIO MPPT performs remarkably in tracking with high accuracy as PSO based MPPT. Last but not the least, I am very grateful to the Arctic Centre for Sustainable Energy (ARC), UiT The Arctic University of Norway, Norway for providing an environment to d

Sensored speed control of brushless DC motor based salp swarm algorithm

This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time

Modelling and Tracking of the Global Maximum Power Point in Shaded Solar PV Systems Using Computational Intelligence

Solar Photovoltaic (PV) systems are renewable energy sources that are environmentally friendly and are now widely used as a source of power generation. The power produced by solar PV varies with temperature, solar irradiance and load. This variation is nonlinear and it is difficult to predict how much power will be produced by the solar PV system. When the solar panel is directly coupled to the load, the power delivered is not optimal unless the load is properly matched to the PV system. In the case of a matched load the variation of irradiance and temperature will change this matching so a maximum peak power point tracking is therefore necessary for maximum efficiency. The complete PV system with a maximum power point tracking (MPPT) includes the solar panel array, MPPT algorithm and a DC-DC converter topology. Each subsystem is modelled and simulated in MATLAB/Simulink environment. The components are then combined with a DC resistive load to assess the overall performance when the PV panels are subjected to different weather conditions. The PV panel is modelled based on the Shockley diode equation and is used to predict the electrical characteristic curves under different irradiances and temperatures. In this dissertation, five MPPT algorithms were investigated. These algorithms include the standard Perturb and Observe (PnO), Incremental conductance (IC), Fuzzy Logic (FL), Particle Swarm Optimisation (PSO) and the Firefly Optimisation (FA). The algorithms are tested under different weather conditions including partial shading. The Particle Swarm and Firefly algorithm performed relatively the same and were chosen to be the best under all test conditions as they were the most efficient and were able to track the global maximum power point under partial shading. The PnO and IC performed well under static and varying irradiance, the PnO was seen to lose track of the MPP under rapid increasing irradiance. The PnO was tested under partial shaded conditions and it was seen that it is not reliable under these conditions. The Fuzzy logic performed better than the PnO and IC but was not as good as the PSO and FA. Since the fuzzy logic requires extensive tuning to converge it was not tested under partial shaded conditions. A DC-DC boost converter interface study between a DC source and the DC load are performed. This includes the steady state and dynamic analysis of the Boost converter. The converter is linearised about its steady state operating point and the transfer function is obtained using the state space averaged model. The simulation results of the complete PV system show that PSO and Firefly algorithm provided the best results under all weather conditions compared to other algorithms. They provided less oscillations at steady state, high efficiency in tracking (99%), quick convergence time at maximum power point and where able to track global power under partial shaded weather conditions for all partial shaded patterns. The Fuzzy logic performed well for what it was tested for which are static irradiance and rapid varying irradiance. The PnO and IC also performed relatively well but showed a lot of ringing at steady state. The PnO failed to track the MPP at certain instances under rapid increasing irradiance and the IC was shown to be unstable at low irradiance. The PnO was not reliable in tracking the global maximum power point under partial shaded conditions as it converged at local maximum power points for some partial shaded patterns

Model-free neural network-based predictive control for robust operation of power converters

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC

Applications of Power Electronics

Power electronics technology is still an emerging technology, and it has found its way into many applications, from renewable energy generation (i.e., wind power and solar power) to electrical vehicles (EVs), biomedical devices, and small appliances, such as laptop chargers. In the near future, electrical energy will be provided and handled by power electronics and consumed through power electronics; this not only will intensify the role of power electronics technology in power conversion processes, but also implies that power systems are undergoing a paradigm shift, from centralized distribution to distributed generation. Today, more than 1000 GW of renewable energy generation sources (photovoltaic (PV) and wind) have been installed, all of which are handled by power electronics technology. The main aim of this book is to highlight and address recent breakthroughs in the range of emerging applications in power electronics and in harmonic and electromagnetic interference (EMI) issues at device and system levels as discussed in ?robust and reliable power electronics technologies, including fault prognosis and diagnosis technique stability of grid-connected converters and ?smart control of power electronics in devices, microgrids, and at system levels

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Design and analysis of current stress minimalisation controllers in multi-active bridge DC-DC converters.

Multi active bridge (MAB) DC-DC converters have attracted significant research attention in power conversion applications within DC microgrids, medium voltage DC and high voltage DC transmission systems. This is encouraged by MAB's several functionalities such as DC voltage stepping/matching, bidirectional power flow regulation and DC fault isolation. In that sense this family of DC-DC converters is similar to AC transformers in AC grids and are hence called DC transformers. However, DC transformers are generally less efficient compared to AC transformers, due to the introduction of power electronics. Moreover, the control scheme design is challenging in DC transformers, due to its nonlinear characteristics and multi degrees of freedom introduced by the phase shift control technique of the converter bridges. The main purpose of this research is to devise control techniques that enhance the conversion efficiency of DC transformers via the minimisation of current stresses. This is achieved by designing two generalised controllers that minimise current stresses in MAB DC transformers. The first controller is for a dual active bridge (DAB). This is the simplest form of MAB, where particle swarm optimisation (PSO) is implemented offline to obtain optimal triple phase shift (TPS) parameters, for minimising the RMS current. This is achieved by applying PSO on DAB steady-state model, with generic per unit expressions of converter AC RMS current and transferred power under all possible switching modes. Analysing the generic data pool generated by the offline PSO algorithm enabled the design of a generic real-time closed-loop PI-based controller. The proposed control scheme achieves bidirectional active power regulation in DAB over the 1 to -1 pu power range with minimum-RMS-current for buck/boost/unity modes, without the need for online optimisation or memory-consuming look-up tables. Extending the same controller design procedure for MAB was deemed not feasible, as it would involve a highly complex PSO exercise that is difficult to generalise for N number of bridges; it would therefore generate a massive data pool that would be quite cumbersome to analyse and generalise. For this reason, a second controller is developed for MAB converter without using a converter-based model, where current stress is minimised and active power is regulated. This is achieved through a new real-time minimum-current point-tracking (MCPT) algorithm, which realises iterative-based optimisation search using adaptive-step perturb and observe (P&O) method. Active power is regulated in each converter bridge using a new power decoupler algorithm. The proposed controller is generalised to MAB regardless of the number of ports, power level and values of DC voltage ratios between the different ports. Therefore, it does not require an extensive look-up table for implementation, the need for complex non-linear converter modelling and it is not circuit parameter-dependent. The main disadvantages of this proposed controller are the slightly slow transient response and the number of sensors it requires

Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 -6 and (PSO)=3.4948 -4

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Maximum power point tracking of partial shaded photovoltaic array using an evolutionary algorithm: a particle swarm optimization technique

 Partial shading is one of the unavoidable complications in the field of solar power generation. Although the most common approach in increasing a photovoltaic (PV) array’s efficiency has always been to introduce a bypass diode to the said array, this poses another problem in the form of multi-peaks curves whenever the modules are partially shaded. To further complicate matters, most conventional Maximum Power Point Tracking methods develop errors under certain circumstances (for example, they detect the local Maximum Power Point (MPP) instead of the global MPP) and reduce the efficiency of PV systems even further. Presently, much research has been undertaken to improve upon them. This study aims to employ an evolutionary algorithm technique, also known as particle swarm optimization, in MPP detection. VC 2014 Author(s)