Maximum Power Point Technique Research Articles

Performance Analysis of an Incremental Conductance MPPT Algorithm for Photovoltaic Systems Under Rapid Irradiance Changes

The control algorithm for tracking the maximum power point (MPPT) is a crucial factor that influences the system's capacity to harness the maximum energy from the sun using photovoltaic. Incremental conductance stands out as one of the most commonly employed MPPT techniques due to its simplicity and low implementation complexity. However, it has various shortcomings, rendering it less effective in adapting to changing solar radiation conditions. Therefore, this study proposed a modification to enhance the algorithm's performance. The proposed modified algorithm is validated through experiments, revealing that the fastest tracking time for the adjusted incremental conductance algorithm was 72 ms, whereas the unmodified algorithm took 84 ms. Additionally, oscillations observed during changes in radiation values were 2 W and 1.66 W for the unmodified algorithm, occurring during decreases and increases in radiation values, respectively. In contrast, the proposed modified incremental conductance algorithm resulted in a consistent oscillation of 1.33 W for each change in radiation value.Furthermore, it can be concluded that the modified incremental conductance algorithm can increase the tracking time as well as reduce oscillations that occur under various conditions of changes in radiation values.

Design Of Dc -Dc Boost Converter for PV System

Out of all types of energies electrical energy has high demand universally. So, technologies are developed to convert other forms of energy into electrical energy. Depletion in fossil fuels forces the world to derive energy from renewable energy sources like solar, wind, biomass, etc.., .out of all renewable energy sources solar energy has more advantages. As long as the sun is present, we can derive electrical energy. So, PV technologies are developed to convert solar energy directly into electrical energy. A PV system converts solar energy directly into electrical energy and various components of the solar system are PV system, DC-DC converter, Maximum power point technique controllers. In PV systems generated voltage is at a very low level. To enhance this low voltage level to required voltage level boost converters are introduces in PV systems.in addition to boost up the generated voltage level to required level, boost converters can also be used in a tracking maximum power along with MPPT technique. NOT all boost converter work for all systems .so for constructing a PV system we have to design a suitable boost converter .in the present work we studied design aspects of dc-dc boost converter, and we designed dc-dc boost converter for a PV system. Key Words: Photovoltaic system Photovoltaic cell modelling Design DC-DC boost Converter

Bounded Region for Tracking by Two Fuzzy Rules Based Perturb and Observe Technique with Fractional Order based Proportional Integral Controller for Photovoltaic Conversion System

Maximum power from PV system is tracked by using Maximum power point (MPP) tracking techniques. In this paper the Two fuzzy rules based perturb and observed (2F-P&O) with Fractional order based PI (FOPI) controller is developed to track the global MPP under Partial shading conditions (PSCs). The two rules based fuzzy systems are implemented to provide the voltage boundaries under which the P&O starts to track GMP. FOPI controller is used to minimize the error and gives an accurate response under PSCs. The performance analysis of 2F-P&O based MPP technique is showing along with cases as diagonal & column based shading patterns, irradiance change conditions under several PSCs. The 2F-P&O with FOPI based technique compared with traditional P&O, Artificial neural network and Particle swarm optimization, Moreover from the comparative analysis 2F-P&O has higher efficiency, less oscillations and convergence time. This paper is validated using Opal-RT simulator and MATLAB/ Simulink Software.

Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet

High renewable energy penetration worsens systems instability. Balancing consumption energy and generation output energy reduces this instability. This paper introduces coordination control to coordinate the flow of electricity between MG buses and to stabilize the system under variable load, generation conditions. The adopted MG regulates the bidirectional DC/AC main converter using digital proportional resonant controllers in a synchronous reference frame. A maximum power point tracker-based boost DC/DC converter enables the wind turbine and solar photovoltaic to harvest maximum power. Traditional methods such as perturb and observe and incremental conductance maximum power trackers cannot solve nonlinearity and inaccurate responses. This work provides a hybrid maximum power tracker strategy to modify the responses of standard maximum power point techniques based on particle swarm optimization-trained adaptive neuro-fuzzy inference system (ANFIS-PSO) to achieve quick and maximum solar power with minimal oscillation tracking. Concerning the management system, this paper adopts a recent meta-heuristic algorithms-based DSM program to modify consumers’ electricity use by shifting the load appliances to off-peak demand periods. The adopted algorithms for DSM are sparrow search algorithm (SSA), binary orientation search algorithm (BSOA), and cockroach algorithm (CA). Finally, based on energy Internet technology, ThingSpeak cloud-based MATLAB is adopted to gather and display real-time data streams and generate graphical analyses. The simulation results reveal that the recommended coordinating control produces quick grid frequency responsiveness and zero steady-state errors. The optimal demand management program minimizes peak energy consumption from 5.2 kWh to 4.6 kWh. All DSM methods cost 439.1 $ per month, compared to 484.4 $ for the nonscheduling load profile.

A Comprehensive Review on Latest State of the Art Practices in MPPT Algorithm

The performance of the solar charge controller depends largely on the maximum power point (MPPT) tracking algorithms. The review paper emphasis on the factors that influence the selection of MPPT, and performance of the charge controller employed in PV system. An efficient MPPT technique can achieve the purpose of energy saving, better extraction of power from solar. This paper presents a literature review on the basic MPPT techniques in detail along with their variants published by various researchers. The detailed study of MPPT techniques for improving the PV system performance, has been presented in this paper with emphasis on design and future MPPT techniques based on Moving Average Filter.

Maximum power point estimation of MSX-60 poly-crystalline standalone solar PV system

In the recent decades, solar photovoltaic (PV) systems are one of the attractive renewable energy sources among the researchers. The maximum power generated by PV arrays varies with the variation in climatic conditions and exhibits non-linear P–V & I–V characteristics. Solar PV systems require external control to extract maximum power as their conversion efficiency is generally very low. The Maximum Power Point Techniques (MPPT) plays an important role to extract maximum power at every instant of time through an algorithm based automatic control. It automatically adjusts the power interface and extracts maximum possible power from PV array under variable irradiance, shading, and under variable temperature. The evaluation of performance of solar PV systems and MPPT techniques is become essential now days to enhance their performance under real–life variable atmospheric conditions. In additions to this, the performance of Perturb & Observe (P&O), one of the mostly used and robust MPPT techniques is analyzed in this work under continuously variable solar irradiance and temperature. Different patterns of variable solar irradiance are considered along with the real–life variable temperature throughout the day. The simulation has been carried out in MATLAB/Simulink 2020a to analyze the performance of P&O MPPT technique. Various performance parameters viz. Pout_max, Pout_avg, Ppv_max, Ppv_avg, Vout_max, Vout_avg, Vpv_max, Vpv_avg and conversion efficiency η are considered to analyze the performance of MPPT technique.

Identification of Photovoltaic Panel MPPT Using Neuro-Fuzzy Model

A photovoltaic (PV) panel produces energy that is influenced by external factors including temperature, irradiation, and the fluctuations in the load related to it. The PV system should perform at maximum power point (MPP) in order to adjust towards the rapidly increasing interest in energy. Because of the changing climatic conditions, it becomes has a limited efficiency. In order to maximize the PV system's efficiency, a maximum power point technique is necessary. In the present paper a maximum power point (MPP) of photovoltaic (PV) panel is designed and simulated to optimize system performance, accurate synthesis model based on the hybrid neural fuzzy systems is proposed to directly obtain the MPP. So, photovoltaic panel (PV) is analyzed with the mathematical model to obtain the training data. Three cases were used to test the identification of the structure proposed. The results show neuro-fuzzy (Sugeno Model) used were efficient in modeling the MPP of our PV panel. The Mean square error (MSE) is used as the fitness function to guarantee that the MSE is small, the algorithm synthesis model is validated by the MPP PV Panel analysis, simulation, and measurements. Neuro-fuzzy models is presented throughout this paper to demonstrate the effectiveness of the method of training suggested.

A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems

The characteristics of a PV (photovoltaic) module is non-linear and vary with nature. The tracking of maximum power point (MPP) at various atmospheric conditions is essential for the reliable operation of solar-integrated power generation units. This paper compares the most widely used maximum power point tracking (MPPT) techniques such as the perturb and observe method (P&O), incremental conductance method (INC), fuzzy logic controller method (FLC), neural network (NN) model, and adaptive neuro-fuzzy inference system method (ANFIS) with the modern approach of the hybrid method (neural network + P&O) for PV systems. The hybrid method combines the strength of the neural network and P&O in a single framework. The PV system is composed of a PV panel, converter, MPPT unit, and load modelled using MATLAB/Simulink. These methods differ in their characteristics such as convergence speed, ease of implementation, sensors used, cost, and range of efficiencies. Based on all these, performances are evaluated. In this analysis, the drawbacks of the methods are studied, and wastage of the panel’s available output energy is observed. The hybrid technique concedes a spontaneous recovery during dynamic changes in environmental conditions. The simulation results illustrate the improvements obtained by the hybrid method in comparison to other techniques.

Enhanced Maximum Power Point Techniques for Solar Photovoltaic System under Uniform Insolation and Partial Shading Conditions: A Review

In the recent past, the solar photovoltaic (PV) system has emerged as the most promising source of alternative energy. This solar PV system suffers from an unavoidable phenomenon due to the fluctuating environmental conditions. It has nonlinearity in I-V curves, which reduces the output efficiency. Hence, the optimum maximum power point (MPP) extraction of the PV system is difficult to achieve. Therefore, for maximizing the power output of PV systems, a maximum power point tracking (MPPT) mechanism, which is a control algorithm that can constantly track the MPP during operation, is required. However, choosing a suitable MPPT technique might be confusing because each method has its own set of advantages and disadvantages. Hence, a proper review of these methods is essential. In this paper, a state-of-the-art review on various MPPT techniques based on their classifications, such as offline, online, and hybrid techniques under uniform and nonuniform irradiances, is presented. In comparison to offline and online MPPT methods, intelligent MPPT techniques have better tracking accuracy and tracking efficiency with less steady state oscillations. Unlike online and offline techniques, intelligent methods track the global MPP under partial shade conditions. This review paper will be a useful resource for researchers, as well as practicing engineers, to pave the way for additional research and development in the MPPT field.

Analysis Study and Comparison of Different Maximum Power Point Techniques for Solar Photovoltaic Systems

The use of control systems for photovoltaic installations significantly increases the generation of electrical energy and the efficiency of their use. Solar power supply systems are highly dependent on various climatic factors and therefore there is a real need to use automatic control systems for their various energy parameters, such as current, voltage and power. One of the promising systems for monitoring the energy characteristics of photovoltaic installations is MPPT systems (systems for monitoring maximum power points). This article explores the impact of various maximum power point (MPP) tracking methods applied to photovoltaic systems. This work uses methods like climb-to-top (HC), incremental conduction (IC), and perturbation and observation (P&O). A model of a photovoltaic module and a DC boost converter with various TMM “machine theory” methods were modeled using Matlab software . A joint simulation between Matlab Simulink software packages is used to establish the TMM method. Co-simulation is performed to take advantage of each program to process certain parts of the system. The response of various TMM methods is evaluated in rapidly changing weather conditions. The results show that IC performed best among the compared TMM methods, followed by P&O and HC TMM methods in both dynamic response and steady-state over most of the normal operating range. The incremental conduction method has the advantage of extracting more power compared to perturbation and observation. Method and climb-to-top method and are therefore more competitive than the other two methods in a photovoltaic system.

Inverter based implementation of maximum power point techniques

The utilization of renewable energy sources is being pushed by both greater environmental consciousness and expanding demand. Recently, solar photovoltaic technology has found increased use for a broader range of applications. This may be ascribed to solar energy's extensive availability as well as its long-term viability and low cost. According to the global photovoltaic (PV) industry, 594 gigawatts (GW) of PV capacity were installed in 2019, with the objective of replacing conventional source-based generating facilities. The major problem in PV production, however, is identifying the maximum power point tracking (MPPT) systems that are currently in use to compute peak output. For 1240 W PV power plants, this article compares perturb & observe MPPT approaches with incremental conductance MPPT techniques. The MATLAB Simulink program was utilized to conduct the study, which was based on many factors such as voltage, current, and output power under various weather conditions. When these MPPT algorithms are applied to solar trackers, the efficiency, reaction time, and steady-state oscillations all improve.

A SIMULATION OF SOLAR ENERGY SYSTEM CONTROLLED BY P&O, IC AND FUZZY LOGIC USING BIDIRECTIONAL CHARGING OF BATTERY

In this study, a simulation designs using a MATLAB is presenting to identify the characteristic of a solar energy panel as main feeding part, also, an alternative source represented by battery to compenstae the required missing energy on the load in case of low energy produced by a PV panel due to the effect of atmosphere condition on the solar energy in case of low radiation and temperature degree fall on it where a PV system as a whole works on feeding both of load and battery, but it may depend on the stored energy into the battery based on its capacity. In this purpose, different maximum power point techniques used in the PV system to extract the maximum energy. Perturb and Observe P& O, Incremental Conductance IC and Fuzzy Logic are the main common and modern techniques for tracking the maximum power

Hardware Implementation of Cooperative Multitasking Control and Stability Analysis for DC Off-Grid System

In this article, the design and implementation of a cooperative multitasking control strategy for standalone photovoltaic (SPV) system is presented. A double-loop control strategy based on the fuzzy logic controller is developed to achieve high performance from the SPV without using the maximum power point technique, balance the power in the system, regulate the common dc-link voltage, and maintain system operation stability under disturbance. To ensure uninterruptible power supply to the critical loads during diminution of generated power from the SPV, turned <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> the diesel generator and the dc dump load, power management technique based on load scheduling technique is developed and implemented using the state-flow approach. Furthermore, the stability analysis in open and closed-loop using the average model of the dc–dc buck-boost converter is investigated. The performance of the dc off-grid configuration based on a single dc–dc converter and their control strategies are performed under load and solar irradiation change using MATLAB/Simulink and in real-time through hardware prototyping with DSP controller TMDS28379D.

Extraction of Maximum Power of Organic Photovoltaic Generator Using MPPT Technique

The amount of energy generated by an Organic Photovoltaic (OPV) system depends mainly on the following.Such as solar temperatures and irradiations. Depending on the high cost and low efficiency of an organic photovoltaic system, it can be operated at the maximum power point (MPPT) that changes with solar radiation, temperature or load variations. This work presents an improved algorithm for tracking the maximum power point (MPPT) of a OPV system under real climatic conditions. The proposed MPPT is based on the perturbation and observation (P&amp;O) strategy and the variable pitch method which controls the load voltage to ensure optimum operating points of a OPV system.

Fault classification for photovoltaic module based on maximum power point and machine learning techniques

Photovoltaic (PV) module is the key component in the solar energy system. Fault classification for photovoltaic module is necessary for safety, efficiency and reliability of photovoltaic systems. When faults of PV module occurred, the current-voltage characteristics (I-V curves) of PV module are changed leads to the shift in voltage and current of the maximum power point (MPP). In this paper, fault classification for photovoltaic module based on MPP and machine learning techniques is proposed. Through analysing the location of the MPP in I-V parameters plane, faults of PV module during performance of could be identified and classified. The parameters of voltage and current of PV module in the faults (line-to-line fault, shading, open circuit in one array, open circuit, short circuit during different irradiation levels) are collected by simulations in Simulink/MATLAB software. Then, these parameters would be processed and built into a dataset. This dataset would be fed into fault classification model for PV module using machine learning algorithms included Support Vector Machine (SVM) and k-means. The desired results of machine learning models would be accurately classified faults using parameters of voltage and current of the MPP and survey the fault diagnosis through the borderline of each classification group of the models. The result shows that the fault classification capability and accuracy of the machine learning model using SVM algorithm (98,9%) are better than the k-means algorithm (61,1%).

A Total-Cross-Tied-Based Dynamic Photovoltaic Array Reconfiguration for Water Pumping System

Most Photo-voltaic (PV) applications require a shadow-free area for the installation of the PV panels. However, shading is inevitable in a few applications like building-integrated PV pumping systems (BIPVPS). Partial shading occurs due to soiling, clouds, shades of nearby trees, buildings, current, and telephone poles. During this partial shading condition, the PV array output power decreases, reducing the efficiency of the BIPVPS. In this paper, a novel dynamic array reconfiguration technique is proposed to enhance the power output from the PV array. The proposed system uses a boost converter with an incremental conductance algorithm for operating enhanced power at the maximum. Sinusoidal Pulse Width Modulation (SPWM) technique based on <inline-formula> <tex-math notation="LaTeX">$V/f$ </tex-math></inline-formula> control operates the induction motor. Thus, the proposed algorithm and maximum power point technique (MPPT) enhance the power output from the PV array and track the global power. The PI and <inline-formula> <tex-math notation="LaTeX">$V/f$ </tex-math></inline-formula> control regulate the dc-link voltage and speed of the Induction motor. Simulations and experimental studies are performed to show the effectiveness of the proposed algorithm. The proposed system results in additional 234 W PV power extraction compared to the conventional system. Thus, making the proposed BIPVPS efficient.

Evolutionary Algorithm Based Z-Source DC-DC Boost Converter for Charging EV Battery

In this paper, efficient charging of electric vehicle battery from a considered renewable solar photovoltaic source with the help of a modified Z source with efficient boosting topology. Adapting this Z-source converter to act as a voltage gainer with a boosting function allows a solar Photovoltaic (PV) input voltage of 25VDC (Volts Direct Current) to be increased to a designed output voltage of 75VDC at a low duty ratio, resulting in minimal switching loss. The closed-loop steady-state and transient parameters at the output were analyzed and compared using modern evolutionary algorithms. The power range upheld throughout the circuit is around (300–350) W. The battery is assumed to have an impedance model of Resistor-Capacitor (RC) load with a serial range of 12 V and 7 Ah. The proposed converter achieves higher conversion efficiency by the Maximum Power Pont Tracking (MPPT) and NSGA-II/MNSGA-II (non-dominated sorting genetic algorithm) based controller algorithm for tuning the optimal design value and is validated in a MATLAB Simulink platform. In this work, we analyze closed-loop systems under the mentioned power range. The MPPT with an algorithm-based controller tends to trigger the switch in the closed-loop system to get the optimized output. The Maximum Power Point (MPP) technique implemented is an incremental conductance method for extracting solar PV power and improving load performance. Consequently, the proposed evolutionary optimization algorithm steady-state ripple factor response of the proposed MNSGA-II has a lower output side, thus achieving around 98% of the controller implementation efficiency.

Power Tracking Capability Enhancement of a Grid-Tied Partially Shaded Photovoltaic System Through MPC Based Maximum Power Point Technique

Power Tracking Capability Enhancement of a Grid-Tied Partially Shaded Photovoltaic System Through MPC Based Maximum Power Point Technique

Artificial Neural Network Controller Strategy for Improving DC Link Voltage of Grid Connected Hybrid PV-Wind Generation Systems

This paper presents a power system, consisting of photovoltaic (PV) station and wind farm integrated by ac bus, connected to the grid. The load gets power from both the sources and maximum power is tracked by maximum power point techniques (MPPT) during any changes in the environment. The paper explores how MPPT techniques help power system in tracking power from PV and wind in the conditions of different solar irradiances and different wind speeds. This paper’s objective is to show the improvement in step response of dc link voltage by artificial neural network (ANN) controller. The control method significantly maintains constant grid voltage ensuring unity power factor even during climatic conditions variation. The whole system is simulated using matlab/simulink software and the results compare the proposed system with existing controller i.e., Proportional Integral (PI). The results show the efficient performance of ANN controller than PI controller.

Wind-driven permanent magnet synchronous generators connected to a power grid: Existing perspective and future aspects

Scholars are motivated to work in the field of renewable energy systems (RESs) especially on grid-connected wind generators because of the exciting and noticeable developments going on in this area. This progress is utilized to obtain the maximal, efficient, and stable electric power from the RESs and integrating it into existing systems to improve its efficiency, stability, reliability, and overall power quality. Recently, permanent magnet synchronous generators (PMSGs) have become the main pillar of advanced wind systems thanks to their fascinating pluses over other types of wind generators. This paper presents the up-to-date trends in converter topologies, control approaches, maximal power production methods, and grid integration issues for PMSG-based wind systems. The performed statistical analyses assure the dominance of the two-level back-to-back converter among the studied power converter topologies, field-oriented control method for the machine side converter, voltage oriented control method for the grid side converter control, perturb and observe algorithm amongst the studied maximum power point techniques, and fault ride-through capability out of grid integration issues. Further, recent general trends in technological advancements for PMSG wind system components are illustrated as a pie chart in terms of percentage figures. It is expected that the researchers working in this field would benefit from this article in terms of the presented state-of-the-art statistical analyses and its related literature given in this study.