Unfilled gap of prolonged energy demand by conventional energy sources and consent of global warming as its vulnerable outcome provides a vent to search compatible option. Augmentation in use of solar energy reveled through last 3 decades portrays its heterogeneous rewards in the prevailing energy scenario. Nevertheless solar PV system arises as viable option in the critical power system era its low efficiency energy conversion attribute necessitates an efficient power conversion system. The nonlinearity of I–V (current–voltage) characteristic and its alteration for an assorted insolation and temperature values may enable the alteration in terminal voltage. This may deviates maximum power point due to which the available maximum power delivery to load can be differed. Literature of this field reiterated that the uniform insolation and partial shading condition demands undeniable need of maximum power point tracking. Nonetheless through investigation in this direction furnishes the availability of a bunch of such techniques; each of them posses its own pros and cones. This ubiquitous trait of available maximum power point tracking (MPPT) techniques unfolds the complexity in its precise selection. To diminish such complexity this paper offers a state of art of various MPPT technique and their comprehensive comparative analysis based on 110 standard research articles. The focus of this paper is to offer a better commencement and to furnish valued information for investigators of this field.

Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems

Given the considerable recent attention to distributed power generation and interest in sustainable energy, the integration of photovoltaic (PV) systems to grid-connected or isolated microgrids has become widespread. In order to maximize power output of PV system extensive research into control strategies for maximum power point tracking (MPPT) methods has been conducted. According to the robust, reliable, and fast performance of artificial intelligence-based MPPT methods, these approaches have been applied recently to various systems under different conditions. Given the diversity of recent advances to MPPT approaches a review focusing on the performance and reliability of these methods under diverse conditions is required. This paper reviews AI-based techniques proven to be effective and feasible to implement and very common in literature for MPPT, including their limitations and advantages. In order to support researchers in application of the reviewed techniques this study is not limited to reviewing the performance of recently adopted methods, rather discusses the background theory, application to MPPT systems, and important references relating to each method. It is envisioned that this review can be a valuable resource for researchers and engineers working with PV-based power systems to be able to access the basic theory behind each method, select the appropriate method according to project requirements, and implement MPPT systems to fulfill project objectives.

State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review

An efficient maximum power point tracking (MPPT) method plays an important role to improve the efficiency of a photovoltaic (PV) generation system. This study provides an extensive review of the current status of MPPT methods for PV systems which are classified into eight categories. The categorisation is based on the tracking characteristics of the discussed methods. The novelty of this study is that it focuses on the key characteristics and eleven selection parameters of the methods to make a comprehensive analysis, which is not considered together in any review works so far. Again, the pros and cons, classification and immense comparison among them described in this study can be used as a reference to address the gaps for further research in this field. A comparative review in tabular form is also presented at the end of the discussion of each category to evaluate the performance of these methods, which will help in selecting the appropriate technique for any specific application.

MPPT methods for solar PV systems: a critical review based on tracking nature

IoT is a revolutionary technology that represents the future of computing and communications. Most of the people over all worlds depend on agriculture. Because of this reason smart IT technologies are needed to migrate with traditional agriculture methods. Using modern technologies can control the cost, maintenance and monitoring performance. Satellite and aerial imagery play a vital role in modern agriculture. Precision agriculture sensor monitoring network is used greatly to measure agri-related information like temperature, humidity, soil PH, soil nutrition levels, water level etc. so, with IoT farmers can remotely monitor their crop and equipment by phones and computers. In this paper, we surveyed some typical applications of Agriculture IoT Sensor Monitoring Network technologies using Cloud computing as the backbone. This survey is used to understand the different technologies and to build sustainable smart agriculture. Simple IoT agriculture model is addressed with a wireless network.

A Survey: Smart agriculture IoT with cloud computing

In this paper, two new Artificial Neural Network MPPT controllers based on fixed and variable step size have been proposed and investigated. The data required to generate the ANN model are generated using the classical Perturbation and Observation algorithm. The neural network MPPT controller is developed in two steps: the offline step required for training of different neural networks parameters in order to find the optimal neural network MPPT controller (structure, activation function and training algorithm) and the Online step where the optimal neural network MPPT controller is used in PV system. The performance of the proposed variable step size and fixed step size ANN-MPPT methods are analyzed under different operating conditions using Matlab/Simulink. To validate the simulated system hardware implementation of the proposed algorithms was carried out using experimental prototype MPPT based on Flyback converter connected to Solarex MSX-60 (4 panels) and dsPIC30F4011 control circuit. Analysis and comparative study between the proposed fixed and variable step size ANN-MPPT controllers have been presented, showing a real contributions in term of tracking accuracy, response time, overshoot and steady state ripple. In addition, this paper can be considered as a review study on ANN-MPPT methods for PV systems.

A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation

In recent decades, Photovoltaic (PV) energy has made significant progress towards meeting the continuously increasing world energy demand. Besides that, the issue of conventional fossil fuels depletion as well as environmental pollution both contribute to the growth of PV technology. However, the deployment and implementation of photovoltaic systems remain as a great challenge, since the PV material cost is still very high. The low PV module conversion efficiency is another factor that restricts the wide usage of PV systems, therefore a power converter embedded with the capability of maximum power point tracking (MPPT) integrated with PV systems is essential to further the technology. This paper provides a comprehensive review of the available MPPT techniques, both the uniform insolation and partial shaded conditions. In order to appreciate the knowledge of MPPT concepts, several types of PV cell equivalent models are explained too. Conventional MPPT techniques have proven the ability to track the maximum power point (MPP) under uniform solar irradiance. However, under rapidly changing environments and partially shaded conditions, conventional techniques have failed to track the true MPP. For this reason, stochastic based methods and artificial intelligence have been developed with the ability to seek the true MPP under multiple peaks with good convergence speed. This paper analyses and compares both conventional and stochastic MPPT techniques based on the true MPP tracking capability, design complexity, cost consideration, sensitivity to environmental change and convergence speed. Comparatively, the stochastic algorithms and artificial intelligence show excellent tracking performance. The research on MPPT techniques is ongoing towards achieving a better performance in terms of the ease of implementation, low system cost and better tracking efficiency.

A comprehensive review of maximum power point tracking algorithms for photovoltaic systems

This paper presents the smart irrigation system using Internet of Things (IoT) via the Arduino Mega 2560. The objectives of this paper are to investigate the concept of smart irrigation system using IoT, to develop a system using an Arduino Mega 2560 that processes the data from the soil sensor which automatically water the plant and to analyse the real time condition of soil of the plants via the smart phone that is connected to the internet. The scope of the study is focused on farming crops and gardening. The limitations of this project can be very expensive if it is applied on a big area. It had to install the sensors for every plant as it is needed to know the condition of the soil. Water pump must also be added for every plant in order to supply the water. In this project, it requires blynk application software on the smartphone and hardware implementation which can detect the condition of the plant by using the dht 11 sensor and moisture level sensor. The findings of this paper are based on the experiments that were done. The first two experiments were between smart irrigation and normal irrigation. After 7 days, the results showed that smart irrigation topple the normal irrigation. Then, it proceeded to the next 3 experiments which are with the appearance of sunlight, pH values of the water and the windy condition. The conclusion of this paper is based on the objectives which all the three objectives had already achieved.

Smart Irrigation System Using Internet of Things

This research focuses on increasing output voltage of the photovoltaic (PV) system while implementing maximum power point tracking (MPPT) technique. In order to increase the output voltage of the system, boost converter is connected to the PV system. However, when connecting boost converter to the PV system the voltage value at the output equal to the input voltage. According to the basic principle of boost converter, output voltage must be higher than the input voltage. The output voltage depends on the duty cycle of the triggering pulse. As to increase the output voltage of PV system based on boost converter characteristic, new boost converter algorithm is implemented with Particle Swarm Optimization (PSO) algorithm for Maximum Power Point Tracking (MPPT) controller. There are several obstacles to extract the maximum power during the power harvesting process, such as partial shading and varying atmospheric condition. Simulation is done by Matlab/ Simulink to identify the best parameters value of boost converter while implementing new coding in the MPPT block to achieve the maximum power from the PV system. This paper proposed the maximum power point tracking on PV panel while implementing boost converter during the condition of partial shading.

Implementing boost converter algorithm with PSO for photovoltaic system during partial shading condition

This paper is discussed about the monitoring system for tripping fault detection based on IoT. The objective of this project is about to study the condition of tripping fault in the electrical line in the building wiring and the safety component that function to encounter the fault tripping problem, to develop the system of fault monitoring system based on internet of thing (IoT) that can ease the person in charge to locate, detect the location of the fault and the last one to analyze the performance of the system. The subjected parameter for this project are current level and voltage characteristic during fault condition and normal condition. This project also introduces the application of IoT in transmitting the information. ACS712 and VDR circuit is used in this project as a sensoring device to sense the value of current and voltage. By application of ESP32 and Arduino, the location of fault can be send in real time condition. Current value and voltage level are display as the parameter to be analyze. Fault detection system via IoT is used by using BLYNK application.

Nur Atharah Kamarzaman

Papers

A comprehensive review of maximum power point tracking algorithms for photovoltaic systems

Smart Irrigation System Using Internet of Things

Implementing boost converter algorithm with PSO for photovoltaic system during partial shading condition

Developing a Monitoring System for Tripping Fault Detection via IoT

A honey badger algorithm for optimal sizing of an AC coupled hybrid stand-alone photovoltaic system