Performance Analysis Of Hybrid Ai-Based Technique For Maximum Power Point Tracking In Solar Energy System Applications

Demand is increasing for a system based on renewable energy sources that can be employed to both fulfill rising electricity needs and mitigate climate change. Solar energy is the most prominent renewable energy option. However, only 30%-40% of the solar irradiance or sunlight intensity is converted into electrical energy by the solar panel system, which is low compared to other sources. This is because the solar power system\u27s output curve for power versus voltage has just one Global Maximum Power Point (GMPP) and several local Maximum Power Points (MPPs). For a long time, substantial research in Artificial Intelligence (AI) has been undertaken to build algorithms that can track the MPP more efficiently to acquire the most output from a Photovoltaic (PV) panel system because traditional Maximum Power Point Tracking (MPPT) techniques such as Incremental Conductance (INC) and Perturb and Observe (P&Q) are unable to track the GMPP under varying weather conditions. Literature (K. Y. Yap et al., 2020) has shown that most AIbased MPPT algorithms have a faster convergence time, reduced steady-state oscillation, and higher efficiency but need a lot of processing and are expensive to implement. However, hybrid MPPT has been shown to have a good performance-to-complexity ratio. It incorporates the benefits of traditional and AI-based MPPT methodologies but choosing the appropriate hybrid MPPT techniques is still a challenge since each has advantages and disadvantages. In this research work, we proposed a suitable hybrid AI-based MPPT technique that exhibited the right balance between performance and complexity when utilizing AI in MPPT for solar power system optimization. To achieve this, we looked at the basic concept of maximum power point tracking and compared some AI-based MPPT algorithms for GMPP estimation. After evaluating and comparing these approaches, the most practical and effective ones were chosen, modeled, and simulated in MATLAB Simulink to demonstrate the method\u27s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values. The AI-based MPPT techniques evaluated include Particle Swarm Optimization (PSO) trained Adaptive Neural Fuzzy Inference System (ANFIS) and PSO trained Neural Network (NN) MPPT. We compared these methods with Genetic Algorithm (GA)-trained ANFIS method. Simulation results demonstrated that the investigated technique could track the GMPP of the PV system and has a faster convergence time and more excellent stability. Lastly, we investigated the suitability of Buck, Boost, and Buck-Boost converter topologies for hybrid AI-based MPPT in solar energy systems under varying solar irradiance and temperature conditions. The simulation results provided valuable insights into the efficiency and performance of the different converter topologies in solar energy systems employing hybrid AI-based MPPT techniques. The Boost converter was identified as the optimal topology based on the results, surpassing the Buck and Buck-Boost converters in terms of efficiency and performance. Keywords—Maximum Power Point Tracking (MPPT), Genetic Algorithm, Adaptive Neural-Fuzzy Interference System (ANFIS), Particle Swarm Optimization (PSO

A Quick Maximum Power Point Tracking Method Using an Embedded Learning Algorithm for Photovoltaics on Roads

This chapter presents a new approach to realize quick maximum power point tracking (MPPT) for photovoltaics (PVs) bedded on roads. The MPPT device for the road photovoltaics needs to support quick response to the shadow flickers caused by moving objects. Our proposed MPPT device is a microconverter connected to a short PV string. For real-world usage, several sets of PV string connected to the proposed microconverter will be connected in parallel. Each converter uses an embedded learning algorithm inspired by the insect brain to learn the MPPs of a single PV string. Therefore, the MPPT device tracks MPP via the perturbation and observation method in normal circumstances and the learning machine learns the relationships between the acquired MPP and the temperature and magnitude of the Sun irradiation. Consequently, if the magnitude of the Sun beam incident on the PV panel changes quickly, the learning machine yields the predicted MPP to control a chopper circuit. The simulation results suggested that the proposed MPPT method can realize quick MPPT

Maximum Power Point Tracking Algorithm for Advanced Photovoltaic Systems

Photovoltaic (PV) systems are the major nonconventional sources for power generation for present power strategy. The power of PV system has rapid increase because of its unpolluted, less noise and limited maintenance. But whole PV system has two main disadvantages drawbacks, that is, the power generation of it is quite low and the output power is nonlinear, which is influenced by climatic conditions, namely environmental temperature and the solar irradiation. The natural limiting factor is that PV potential in respect of temperature and irradiation has nonlinear output behavior. An automated power tracking method, for example, maximum power point tracking (MPPT), is necessarily applied to improve the power generation of PV systems. The MPPT methods undergo serious challenges when the PV system is under partial shade condition because PV shows several peaks in power. Hence, the exploration method might easily be misguided and might trapped to the local maxima. Therefore, a reasonable exploratory method must be constructed, which has to determine the global maxima for PV of shaded partially. The traditional approaches namely constant voltage tracking (CVT), perturb and observe (P&O), hill climbing (HC), Incremental Conductance (INC), and fractional open circuit voltage (FOCV) methods, indeed some of their improved types, are quite incompetent in tracking the global MPP (GMPP). Traditional techniques and soft computing-based bio-inspired and nature-inspired algorithms applied to MPPT were reviewed to explore the possibility for research while optimizing the PV system with global maximum output power under partially shading conditions. This paper is aimed to review, compare, and analyze almost all the techniques that implemented so far. Further this paper provides adequate details about algorithms that focuses to derive improved MPPT under non-uniform irradiation. Each algorithm got merits and demerits of its own with respect to the converging speed, computing time, complexity of coding, hardware suitability, stability and so on

Evolution engine technology in exhaust gas recirculation for heavy-duty diesel engine

In this present year, engineers have been researching and inventing to get the optimum of less emission in every vehicle for a better environmental friendly. Diesel engines are known reusing of the exhaust gas in order to reduce the exhaust emissions such as NOx that contribute high factors in the pollution. In this paper, we have conducted a study that EGR instalment in the vehicle can be good as it helps to prevent highly amount of toxic gas formation, which NOx level can be lowered. But applying the EGR it can lead to more cooling and more space which will affect in terms of the costing. Throughout the research, fuelling in the engine affects the EGR producing less emission. Other than that, it contributes to the less of performance efficiency when vehicle load is less

A New Modified MPPT Controller for Indirect Vector Controlled Induction Motor Drive with Variable Irradiance and Variable Temperature

Due to the increase in power demand and the earth natural resources are depleting day by day, renewable energy sources have become an important alternate and solar energy is mainly used. In order to track the radiations from the sun in an efficient manner the maximum power point tracking (MPPT) controller is used. But the existed MPPT controllers were developed based upon the ideal characteristics of constant irradiation and temperature. To overcome the above problem a practical data is considered for designing of MPPT controller which is based upon variable irradiance under various temperatures. The output obtained from the MPPT is given to the boost converter with an inverter to find the performance of an indirect vector controlled induction motor drive under different operating conditions. For inverter control, a SVM algorithm in which the calculation of switching times proportional to the instantaneous values of the reference phase voltage. It eliminates the calculation of sector and angle information. The torque ripple and the performance of induction motor drive with ideal and practical data MPPT controllers are compared under different operating conditions. An experimental validation is carried out and the comparison is made with the simulation results. Keywords: maximum power point tracking, variable irradiance, indirect vector controlled, total harmonic distortions, space vector modulation, induction motor drive and torque ripple

Modelling and optimisation of solar voltaic system using fuzzy logic

There is considerable increase in residential solar grid connected installations with many advantages offered by solar energy. As more solar panels are connected to grid, the Solar Inverter between solar panels and grid have to perform at optimum levels. Modern Inverters consist of DC-DC Converter and DC-AC Inverter. One problem associated with Inverter design is voltage fluctuation, this defect lies in the DC-DC converter Maximum power tracking (MPPT) algorithms responsible for extracting maximum power from the solar panels. The defect is due to large sampling number required for conventional MPPT algorithm. This thesis has proposed a new MPPT algorithm based on Mamdani Fuzzy logic. In research we use 5 parameter one diode model for solar cell modelling. The P-V/I-V characteristics curve is generated. The P-V characteristics curves sectioned and input membership and output membership functions is created. And unique fuzzy rules is used to optimize fuzzy controller output. Mamdani Fuzzy logic algorithm is compared to traditional PI controller hill climbing method. When small sampling number is used hill climbing method response is slow and good at tracking. When big sampling number is used hill climbing method response is fast and not good at tracking. The voltage also fluctuates when sampling number is big. Fuzzy logic provides a compromised solution with best response time and moderate tracking accuracy compared to hill climbing method. Fuzzy Logic based DC-DC converter together with PLL and Recursive Discrete Fourier Transform (RDFT) DC-AC inverter synchronization algorithm is employed and simulated in matlab. The MPPT simulation is conducted for a realistic 2.5KW solar panels in a 8 x 2 Matrix. In addition the MPPT algorithm is analyzed to see if it performs under power quality and voltage level tolerance of utility grid requirements. The Fuzzy Logic MPPT is excellent at tracking power. When temperature is fixed and irradiance is varied, the maximum tracking error is 5.2% in all scenarios with one exception. When irradiance is fixed and temperature varied, the maximum tracking error is 1.98%. Furthermore the Fuzzy Logic MPPT meets the power quality and voltage level tolerance requirements of utility grid for irradiance over 600 W/m2. Power quality and voltage level tolerance requirements for irradiance under 600 W/m2 is not critical as this is outside twilight conditions. Out of all the Synchronization algorithm identified in this Thesis, RDFT achieves synchronization very quickly and in addition it suppresses harmonics and noise. The possibility of future study to extend MPPT is also briefly discussed. The extension of future study is using Takagi-Sugeno fuzzy logic. Takagi-Sugeno uses more sophisticated inference and rule evaluation mathematics

Advanced control and optimisation of DC-DC converters with application to low carbon technologies

Prompted by a desire to minimise losses between power sources and loads, the aim of this Thesis is to develop novel maximum power point tracking (MPPT) algorithms to allow for efficient power conversion within low carbon technologies. Such technologies include: thermoelectric generators (TEG), photovoltaic (PV) systems, fuel cells (FC) systems, wind turbines etc. MPPT can be efficiently achieved using extremum seeking control (ESC) also known as perturbation based extremum seeking control. The basic idea of an ESC is to search for an extrema in a closed loop fashion requiring only a minimum of a priori knowledge of the plant or system or a cost function. In recognition of problems that accompany ESC, such as limit cycles, convergence speed, and inability to search for global maximum in the presence local maxima this Thesis proposes novel schemes based on extensions of ESC. The first proposed scheme is a variance based switching extremum seeking control (VBS-ESC), which reduces the amplitude of the limit cycle oscillations. The second scheme proposed is a state dependent parameter extremum seeking control (SDP-ESC), which allows the exponential decay of the perturbation signal. Both the VBS-ESC and the SDP-ESC are universal adaptive control schemes that can be applied in the aforementioned systems. Both are suitable for local maxima search. The global maximum search scheme proposed in this Thesis is based on extensions of the SDP-ESC. Convergence to the global maximum is achieved by the use of a searching window mechanism which is capable of scanning all available maxima within operating range. The ability of the proposed scheme to converge to the global maximum is demonstrated through various examples. Through both simulation and experimental studies the benefit of the SDP-ESC has been consistently demonstrated

An intelligent controlling method for battery lifetime increment using state of charge estimation in PV-battery hybrid system

In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly

Development of Maximum Power Extraction Algorithms for PV system With Non-Uniform Solar Irradiances

This thesis addresses the problem of extraction of maximum power from PV arrays subjected to non-uniform solar irradiances e.g partial shading. In the past, a number of maximum power point tracking algorithms (MPPTs) such as Perturb & Observe, Hill climbing, Incremental Conductance, etc. have been proposed. These are extensively used for obtaining maximum power from a PV module to maximize power yield from PV systems under uniform solar irradiance. However, these techniques have not considered partial shading conditions and the stochastic nature of solar insolation. In the event of non-uniform solar insolation, a number multiple maximum power points (MPPs) appear in the power-voltage characteristic of the PV module. In the present thesis, the stochastic nature of the solar insolation is considered to obtain the global MPP of a PV module with a focus on developing global optimization techniques for MPPT that would handle the multiple MPPs. Thus, the thesis will address the above problem by developing a number of global MPPT algorithms. In this thesis, an extensive review on MPPT algorithms for both uniform and non-uniform insolation levels is presented. Subsequently, an analysis with respect to their merits, demerits and applications have been provided in order to design new MPPTs to achieve higher MPPT efficiency under non-uniform solar irradiances. Firstly, PV modules are modelled with and without bypass diodes for handling Partial shading conditions (PSCs). Then, a new Ring pattern (RP) configuration has been proposed which is compared with different existing configurations such as Series parallel (SP), Total cross tied(TCT) and Bridge linked(BL) configurations on the basis of maximum power and fill factor. As described earlier, under non-uniform irradiances the MPPT problem boil down to determining the global MPP. Thus, the MPPT problem can be cast as a global optimization problem. It may be noted that evolutionary computing approaches are extensively used for obtaining global optimum solutions. One of the most recent evolutionary optimization techniques called grey wolf optimization technique has gained enormous popularity as an efficient global optimization approach. In view of this, Grey wolf optimization is employed to design a global MPPT such that maximum power from PV modules can be extracted which will work under partial shading conditions. Its performance has been compared with two existing MPPTs namely P&O and IPSO based MPPT methods. From the obtained simulation and experimental results, it was found that the GWO based MPPT exhibits superior MPPT performance as compared to both P&O and IPSO MPPTs on the basis of dynamic response, faster convergence to GP and higher tracking efficiency. Further, in order to scale down the search space of GWO which helps to speed up for achieving convergence towards the GP, a fusion of GWO-MPPT with P&O MPPT for obtaining maximum power from a PV system with different possible patterns is developed. An experimental setup of 600W solar simulator is used in the laboratory having characteristics of generating partial shading situation. Firstly, the developed algorithms were implemented for a PV system using MATLAB/SIMULINK. Subsequently, the aforesaid experimental setup is used to implement the proposed global MPPT algorithms. From the obtained simulation and experimental results it is observed that the Hybrid-MPPT converges to the GP with least time enabling highest possible maximum power from the solar PV system. In this thesis, analytical modeling of PV modules for handling non-uniform irradiances is pursued as well as a new RP configuration of PV modules is developed to achieve maximum power and fill factor. In order to extract maximum power from PV panels subjected to non-uniform solar irradiances, two new MPPT algorithms are developed namely Grey wolf optimization based MPPT (GWO-MPPT) and GWO assisted PO (GWO-PO)