Power Point Tracking Algorithm Research Articles

A Comprehensive Review of Flexible Power-Point-Tracking Algorithms for Grid-Connected Photovoltaic Systems

The rapid increase in the penetration of photovoltaic (PV) power plants results in an increased risk of grid failure, primarily due to the intermittent nature of the plant. To overcome this problem, the flexible power point tracking (FPPT) algorithm has been proposed in the literature over the maximum power point tracking (MPPT) algorithm. These algorithms regulate the PV power to a certain value instead of continuously monitoring the maximum power point (MPP). The proposed work carries out a detailed comparative study of various constant power generation (CPG) control strategies. The control strategies are categorized in terms of current-, voltage-, and power-based tracking capabilities. The comparative analysis of various reported CPG/FPPT techniques was carried out. This analysis was based on some key performance indices, such as the type of control strategy, irradiance pattern, variation in G, region of operation, speed of tracking, steady-state power oscillations, drift severity scenario, partial shading scenario, implementation complexity, stability, fast dynamic response, robustness, reactive power, cost, and tracking efficiency. Among existing FPPT algorithms, model-based control has a superior performance in terms of tracking speed and low steady-state power oscillations, with a maximum tracking efficiency of 98.57%.

Automated Solar PV Simulation System Supported by DC–DC Power Converters

Solar photovoltaic simulators are valuable tools for the design and evaluation of several components of photovoltaic systems. They can also be used for several purposes, such as educational objectives regarding operation principles, control strategies, efficiency, maintenance, and other aspects. This paper presents an automated solar photovoltaic simulation system with the capability to generate automated tests considering different parameters of solar photovoltaic panels and different operation conditions. The proposed simulator is composed of three buck-boost DC–DC power converters controlled in such a way that will behave similarly to solar photovoltaic panels. It allows to introduce additional variable loads and maximum power point tracker algorithms similar to real systems. Some converters are controlled by a DSP microcontroller connected to a single programmable logic controller which generates the automated tests. Thus, using the presented solution, it is possible to implement the I-V and P-V characteristic curves of solar photovoltaic panels and evaluate different maximum power point tracker algorithms considering different meteorological conditions and load variations, being a useful tool to teach subjects related to renewable energy sources and related applications. Several simulation results using Matlab/Simulink and experimental results are presented to validate the operation of the proposed solution. Experimental results achieve a ripple between 2% and 5% of the desired average current in MPP conditions.

Improved particle swarm optimization algorithm for photovoltaic system under local shading

ABSTRACT To solve the prediction accuracy problem of the commonly used photovoltaic power generation model in industry, the theoretical model of photovoltaic power generation is improved based on the experimental data in this paper. Furthermore, the influences of the photovoltaic module output characteristics and local shading on the power generation efficiency of series photovoltaic modules were analyzed using MATLAB simulations. Finally, to solve the maximum power point tracking problem of the traditional maximum power point tracker algorithm under local shading, an improved particle swarm optimization (PSO) algorithm based on a linearly decreasing inertia weight w and a learning scale factor δ is proposed in this paper. The simulation results showed that the proposed algorithm tracked the power 1.45 times faster than the PSO algorithm, had 5.8 and 5.09 times more power stability at the working points than the PSO and perturb and observe (P&O) algorithms, respectively, and had 1.12 and 1.25 times more power at working points than the PSO and P&O algorithms, respectively.

Multi-Objective Spotted Hyena Optimization-Based Control of Grid-Tied PV System

This article describes the multi-objective spotted hyena optimization (MOSHO) based control of a three-phase three-wire grid-tied dual-stage PV system. The MOSHO delivers an optimized step size ( δ ) for the incremental conductance (InC) based maximum power point tracking (MPPT) algorithm, as well as optimizes the PI controller gains for DC bus voltage ( V d c ) regulation. The δ optimized MPPT facilitates the faster and more accurate tracking of maximum power point with fewer fluctuations at steady-state. The MOSHO optimized PI controller regulates the V dc and delivers better stability with reduced V dc variations in DC bus voltage during steady-state and dynamic states, i.e. irradiation variation, abnormal and unbalanced grid voltage. The optimized DC bus generates an accurate loss component of current, which further enhances the voltage source converter (VSC) performance in terms of precise reference currents generation and power quality improvement. The proposed system performs satisfactorily in a MATLAB simulation environment as per IEEE519 standards.

Takagi–Sugeno State Delayed Feedback and Integral Control for PV Systems: Modeling, Simulation, and Control

The integration of the large-scale photovoltaic systems has experienced significant growth, which is similarly expected to occur with small-scale photovoltaic systems. Since small-scale systems must be simple in cost-effective components, control strategies must be implemented in low complexity circuits. However, current maximum power point tracking (MPPT) algorithms are generally complex and require electronic components to support variable control gains for different irradiance conditions, preventing simple MPPT implementations suitable for small-scale photovoltaic systems. This paper proposes a new control strategy to tackle the power tracking problem of the power systems. First, a dynamic model of the photovoltaic system is described and converted into a Takagi–Sugeno (T-S) model. Then, an MPPT scheme is proposed in series with a fixed integral and a fuzzy gain state delay feedback controller, which avoids the need for a variable control gain, simplifying the electronic implementation of the control strategy. New delay-dependent stabilization conditions based on the Lyapunov-Krasovskii functional are proposed in terms of a convex optimization problem, where the delayed feedback and integral gains are designed simultaneously. Simulation results using Matlab and Simulink are used to validate the proposed method.

GMPPT Algorithm Based Maximum Power Tracking under Dynamic Weather Conditions Employing Krill-Herd Technique

ABSTRACT As solar energy extraction becomes more and more popular, attempts are made to further improve the efficiency and reliability of such systems. At the time of partially shaded conditions, the peak power that can be extracted from the PV modules may be more, but the conventional control algorithms may result in the underutilization of the panel. The Maximum Power Point Tracking (MPPT) algorithms like Perturb and Observance (P&O), Incremental Conductance, and other conventional methods fail to detect the global maximum power point (GMPP). In such conditions, a GMPP algorithm is recommended. This paper proposes a new GMPP algorithm called Krill-Herd (K-H) to augment the performance of PV modules with non-uniform irradiations. Krill is a marine animal, and researchers have shown keen interest in this herd due to its ability to form large swarms. The hardware experimentation with a highly efficient Interleaved Boost Converter (IBC) with GaN devices under different partially shaded conditions is used to validate the proposed algorithm. IBC has many advantages over conventional boost converters such as low input current ripple, high efficiency, fast transient response, and improved reliability, less current stress on switching devices, and reduction in filter size. The K-H algorithm shows improved performance over the conventional P&O based algorithms with no oscillations around peak power point, less time to reach steady-state, less power loss, more accuracy, and fewer numbers of iterations. Five different conditions of operation of the panels in real time applications have been considered and is demonstrated that higher efficiency levels of around 99% could be obtained using the K-H algorithm in all these cases.

A Novel High Step-Up DC–DC Converter with Improved P&O MPPT for Photovoltaic Applications

In this research work, a Maximum Power Point Tracking (MPPT) algorithm is proposed that improves the tracking accuracy and efficiency of the conventional Perturb and Observe (P&O) algorithm. Due to steady-state oscillations and passive dynamics, the efficiency of conventional P&O algorithm is significantly affected in varying climatic conditions. Therefore, to improve its efficiency and tracking accuracy, a reference voltage is defined as a function of irradiance and temperature in the proposed Improved Perturb and Observe (IP&O) algorithm. Moreover, the solar irradiance and temperature of the PV panel are estimated that results in low steady-state oscillations and fast dynamic response. Furthermore, to step-up a low generated PV voltage to a high voltage level, a non-isolated DC–DC converter suitable for large voltage conversion ratios is also designed. The proposed converter uses only one switch that makes its circuitry and control simple and makes it very feasible for PV applications. This converter attains a high voltage gain with relatively low voltage stress on a switch compared with the conventional or other newly developed topologies that have the same or lesser components count. To validate the theoretical concepts of the DC–DC converter and the IP&O algorithm, simulations are carried out in MATLAB/Simulink environment at varying operating climatic conditions. The simulation results and the comparative examination with the conventional P&O algorithm validate the proposed algorithm's adequate performance.

Optimal control of grid-connected microgrid PV-based source under partially shaded conditions

Microgrids are gaining increasing attention globally and becoming increasingly powered by photovoltaic (PV) systems, thereby requiring high-efficiency control to function as a microgrid distributed generation unit. Accordingly, this study presents an optimal control of a grid-connected Microgrid PV Source (MPVS) under partially shaded conditions. The objective is to ensure the MPVSs ability to rapidly and precisely deliver the amount of power assigned by the supervisory controller. Thus, MPVS must shift rapidly and smoothly between the maximum and intermediate power point modes. The proposed system is composed of PV array, grid emulators, and two converters coupled to a common DC bus. The control strategy of the boost converter is based on the combination of two algorithms: particle swarm optimization algorithm and the proposed intermediate power point tracker algorithm. The voltage source inverter is controlled to keep the DC bus voltage constant and inject the power to the grid, in which the voltage-oriented control technique is applied and combined with the phase-locked loop algorithm for voltage synchronization. Lastly, all control algorithms are implemented in a DSpace 1104 environment and largely tested under various partially shaded patterns.

Deep neural network based MPPT algorithm and PR controller based SMO for grid connected PV system

ABSTRACT In a grid connected photovoltaic (PV) system, the maximum power will be tracked by the conventional Perturb and Observe (P&O) algorithm. It generally produces high oscillations around maximum power point, and it fails to attain extreme power under prompt environmental conditions. To overcome these issues, this work proposes a deep neural network (DNN) based Maximum Power Point tracking (MPPT) algorithm and PR controller-based SMO for PV grid-connected system. The DNN consists of many algorithms in that here we use RNN in MPPT algorithm for tracking the maximum power. The Recurrent Neural Network (RNN) hidden layer has more number of layers. The deep network weight function is optimised by meta-heuristic-based Bald Eagle Search (BES) Optimisation. The proposed tracked RNN based Maximum Power Point (MPP) is passed through the DC/DC buck-boost converter. The inverter is tuned by Proportional Resonant (PR) controller, and it has two gain parameters like a proportional and integral gain. The PR controller gain parameters are tuned by the global optimisation algorithm of Spider Monkey Optimisation (SMO) to achieve better performance.

Modelling and Analysis of Photovoltaic System under Partially Shaded Conditions using Improved Harmony Search Algorithm

With the increasing penetration of solar electricity in residential, institutional and commercial centres around the globe, the phenomenon of partial shading (PS) in Photovoltaic (PV) power generation is gaining attention. Under Partial shading condition (PSC), cells that are shaded tends to have an equivalent current with cells that are unshaded in series-connection, due to this, the shaded cells operates in reverse bias and consequently becomes load and consumes the generated power. This causes a serious problem known as hotspot. This is characterized by the presence of excessive heat which consequently reduces the total generated power. Recently, researchers use the technique of bypass diodes across the PV cells so that the problem of partial shading can be reduced, but this solution taken alone, has made the nonlinearity and complexity of the system to increase. The shaded cells generate multiple peaks with only one global peak. Conventional Maximum Power Point Tracking (MPPT) algorithms do not differentiates the global peak from local peaks which may end up tracking local peak as global peak, this results in serious power loss. This paper seeks to solve this problem by modelling a PV system under PSC and through the application of Improved Harmony Search algorithm (IHSA) and variable step Perturb & Observe (P&O) to track the global peak instead of local peaks. Simulation was done in MATLAB/Simulink 2018a environment, and the results under standard test condition (STC) and PSC showed that the proposed IHSA had an improvement of 25%, 3.17% and 2.27%, 3.07% and 2.21%, 3.26% and 2.26% when compared with the improved particle swarm optimization (IPSO) under STC and PSC conditions respectively, which had a better advantage of minimizing power oscillation and improving the efficiency of the system, improved MPPT tracking, reduced error and a better tracking efficiency in both conditions.
 Keywords: MPPT, photovoltaic system, partial shading, tracking efficiency, Harmony search algorithm

A New MPPT Algorithm for Photovoltaic Power Generation under Uniform and Partial Shading Conditions

During its operation, a photovoltaic system may encounter many practical issues such as receiving uniform or non-uniform irradiance caused mainly by partial shading. Under uniform irradiance a photovoltaic panel has a single maximum power point. Conversely under non-uniform irradiance, a photovoltaic panel has several local maximum power points and a single global maximum power point. To maximize energy production, a maximum power point tracker algorithm is commonly implemented to achieve the maximum power operating point of the photovoltaic panel. However, the performance of the algorithm will depend on operating conditions such as variation in irradiance. Presently, most of existing maximum power point tracker algorithms work only in a single condition: either uniform or non-uniform irradiance. This paper proposes a new maximum power point tracker algorithm for photovoltaic power generation that is designed to work under uniform and partial shading irradiance conditions. Additionally, the proposed maximum power point tracker algorithm aims to provide: (1) a simple math algorithm to reduce computational load, (2) fast tracking by evaluating progress for every single executed duty cycle, (3) without random steps to prevent jumping duty cycle, and (4) smooth variable steps to increase accuracy. The performances of the proposed algorithm are evaluated by three conditions of uniform and partial shading irradiance where a targeted maximum power point is located: (1) far from, (2) near, and (3) laid between initial positions of particles. The simulation shows that the proposed algorithm successfully tracks the maximum power point by resulting in similar power values in those three conditions. The proposed algorithm could handle the partial shading condition by avoiding the local maxima power point and finding the global maxima power point. Comparisons of the proposed algorithm and other well-known algorithms such as differential evolution, firefly, particle swarm optimization, and grey wolf optimization are provided to show the superiority of the proposed algorithm. The results show the proposed algorithm has better performance by providing faster tracking, faster settling time, higher accuracy, minimum oscillation and jumping duty cycle, and higher energy harvesting.

TEG Cascaded Solar PV System with Enhanced Efficiency by Using the PSO MPPT Boost Converter

Thermoelectric generators (TEGs) are used in small power applications to generate electrical energy from waste heats. Maximum power is obtained when the connected load to the ends of TEGs matches their internal resistance. However, impedance matching cannot always be ensured. Therefore, TEGs operate at lower efficiency. For this reason, maximum power point tracking (MPPT) algorithms are utilized. In this study, both TEGs and a boost converter with MPPT were modeled together. Detailed modeling, simulation, and verification of TEGs depending on the Seebeck coefficient, the hot/cold side temperatures, and the number of modules in MATLAB/Simulink were carried out. In addition, a boost converter having a particle swarm optimization (PSO) MPPT algorithm was added to the TEG modeling. After the TEG output equations were determined, the TEG modeling was performed based on manufacturer data sheets. Thanks to the TEG model and the boost converter with PSO MPPT, the maximum power was tracked with a value of 98.64% and the power derived from the TEG was nearly unaffected by the load changes. The power outputs obtained from the system with and without MPPT were compared to emphasize the importance of MPPT. These simulation values were verified by using an experimental setup. Ultimately, the proposed modeling provides a system of TEGs and a boost converter having PSO MPPT.

A Firework-Based GMPPT with Variable Sampling Time for PV Systems

Photovoltaic systems have a nonlinear characteristic in which there is one optimum operating point called Maximum Power Point (MPP). However, when PV panels are partially shaded by surrounding objects, there are several MPPs, of which one of them is Global MPP (GMPP). Therefore, conventional Maximum Power Point Tracking (MPPT) algorithms get trapped into local MPPs. As a result, a multitude of Global MPPT (GMPPT) algorithms have been proposed. An outstanding GMPPT algorithm as well as the fast-tracking speed should find GMPP in complicated shading patterns where not only there are lots of MPPs, but also the peaks are close together. Therefore, in this paper, a novel GMPPT based on firework algorithm is proposed which is able to find GMPP in complicated shading patterns with fast tracking speed. Moreover, the firework is combined with Perturb-and-Observe (P&O) algorithm to reduce the computational effort in a way that the firework is only used to recognize GMPP; afterwards, P&O algorithm completes the tracking. Furthermore, the variable sampling time technique, based on the system settling time, speeds up the tracking process considerably. Finally, the proposed method is compared with previous works, simulated, and implemented on an experimental setup to prove its superiority.

Energy Storage System Based on Low Power Photovoltaic Power Generation

The IU and PU characteristic curves of PCs output in photovoltaic generation (PVG) were analyzed according to the working principle of photovoltaic cells (PCs) in the study, so as to provide a theoretical basis for the further improvement of the maximum power point tracking (MPPT) algorithm. The three-stage charging method was used to charge the battery to improve the energy conversion efficiency in the charging process of the system. The variable step incremental conductance algorithm (ICA) was proposed, which can effectively increase the speed of MPPT in the system and enhance its stability. By setting up a test platform, verification experiments of the tracking algorithm were performed on sunny (higher light intensity), cloudy (medium light intensity), and rainy (lower light intensity) days. The results showed that the adoption of MPPT algorithm can effectively increase the output power (OP) of PCs, the OP in sunny days was higher than in cloudy days and OP in cloudy days was higher than in rainy days compared with the system without MPPT algorithm. Compared with the traditional incremental conductance algorithm, the output power of the PCs using the MPPT algorithm improved by the incremental conductance algorithm had increased by 5.86%. The feasibility and effectiveness of the energy storage system in the study were verified.

A Novel Strategy of Maximum Power Point Tracking for Photovoltaic Panels Based on Fuzzy Logic Algorithm

From the perspective of renewable energy industry investment, absorbing maximum power from renewable sources is a vital factor. Hence, an algorithm is required to change the operating point of renewable energy sources in different environment conditions accordingly. This paper proposes a novel algorithm for tracking the Maximum Power Point (MPP) of a Photovoltaic (PV) panel. In this paper, an auxiliary parameter based on the voltage and power of the PV panel is suggested. By adopting this parameter, independence of irradiation and temperature, the interval between the operating point and the MPP can be estimated. Furthermore, the range of the MPP voltage variations is calculated with respect to various irradiations and temperatures. Then, a novel fuzzy logic Maximum Power Point Tracking (MPPT) algorithm is proposed based on the introduced parameter and voltage variations interval of the MPP. The proposed algorithm has appropriate respond to environment condition changes with proper speed and accuracy. In addition, unlike Hill Climbing (HC) and Perturb and Observe (P&O), the proposed method has no chattering in steady state. The abovementioned claims are successfully validated via the software MATLAB/Simulink.

Performance Study of Maximum Power Point Tracking (MPPT) Based on Type-2 Fuzzy Logic Controller on Active Dual Axis Solar Tracker

World energy consumption increases with time, so that occur an energy imbalance. Many breakthroughs have developed to utilize renewable energy. The photovoltaic system is one of the easy-to-use renewable energies. The power conversion from PV fixed is still low, so the PV system is designed using the active dual-axis solar tracker. The PV tracker position can be adjusted to change the sun position to get maximum efficiency. The active dual-axis solar tracker system is integrated with the maximum power point tracking (MPPT) algorithm to keep PV operating at a maximum power point even though input variations change. The active dual-axis solar tracker system integrated with the maximum power point tracking (MPPT) algorithm to keep PV operating at a maximum power point even though input variations change. Tracking test simulation had done by comparing the output power of a fixed PV system with the active dual-axis solar tracker. Type-2 fuzzy logic based MPPT successfully increased the average output power by 10.48 % with the highest increase of 17.48 % obtained at 15:00 West Indonesia Time (GMT+7). The difference in power from a fixed PV system with the active dual-axis solar tracker of 36.08 W is from the output power worth 206.3 to 242.4 W.

Variable structure power control under different operating conditions of PM synchronous generator wind farm connected to electrical network

The enlarged penetration of wind power into electrical network brings challenges to control strategies of variable speed wind energy conversion systems (VS-WECS). In this study, a nonlinear control scheme for wind farm system (WFS) is proposed. The control technique is realised using variable structure sliding mode control (VSSMC). The generator side rectifiers control is used to keep the generator rotor velocities at an optimal value obtained from the maximum power point tracking (MPPT) algorithm to maximise the total extracted power. The grid-side inverter injects the generated power into the AC network, regulates DC-link voltage and it is used to achieve unity power factor. The simulation results demonstrate the effectiveness of the proposed VSSMC strategy in different scenarios, and their advantages are shown in comparison with a conventional proportional integral (PI) control approach under grid fault conditions and the possible presence of uncertainties.

DESIGN AND IMPLEMENTATION OF ENHANCED PSO BASED MPPT FOR PV PRODUCTION UNDER PARTIAL SHADING CONDITIONS

The search for cleaner energy solutions is being encouraged by the increasing world's energy demand and the emerging environmental concerns. Renewable sources are free, clean and virtually limitless and for those reasons they present a great potential. Photovoltaic systems (PV) have low operation and maintenance costs and to increase the efficiency of a PV production, a Maximum Power Point Tracking (MPPT) algorithm is proposed based on the particle swarm optimization (PSO) algorithm. The proposed PSO-based MPPT is able to avoid the oscillations around the maximum power point (MPP) and the convergence to a local maximum under partial shading conditions (PSC). Experimental and simulations tests were done to evaluate the performance of the proposed algorithm. The results show that it exhibits an excellent tracking under rapid variation in environment conditions (irradiance), no oscillations once the MPP is found and it can avoid the convergence to local maxima.

A Control Scheme without Sensors at the PV Source for Cost and Size Reduction in Two-Stage Grid Connected Inverters

In order to reduce the cost of PV facilities, the market requires low cost and highly reliable PV inverters, which must comply with several regulations. Some research has focused on decreasing the distortion of the current injected into the grid, reducing the size of the DC-link capacitors and removing sensors, while keeping a good performance of the maximum power point tracking (MPPT) algorithms. Although those objectives are different, all of them are linked to the inverter DC-link voltage control loop. Both the reduction of the DC-link capacitance and the use of sensorless MPPT algorithms require a voltage control loop faster than that of conventional implementations in order to perform properly, but the distortion of the current injected into the grid might rise as a result. This research studies a complete solution for two-stage grid-connected PV inverters, based on the features of second-order generalized integrators. The experimental tests show that the proposed implementation has a performance similar to that of the conventional control of two-stage PV inverters but at a much lower cost.

Design and Implementation of P&O Maximum Power Point Tracker Algorithm Based Solar Charge Controller

: Solar energy is one of the main sources of renewable energy. In photovoltaic systems, energy is obtained from photovoltaic batteries In photovoltaic batteries, the current and voltage do not increase linearly. This causes the power to take different values. There is a point where power reaches its peak. Detecting this point called the maximum power point and transferring the energy to the load at this point, reduces the losses and increases the efficiency. Many microcontroller controlled circuits have been designed and algorithms have been developed for maximum power point trackingPerturb and Observe algorithm is the most widely used algorithm. In this study; Maximum Power Point Tracker Algorithm Based Solar Charge Controller is designed. Arduino microcontroller card was used as it has easy programmability and rapid prototype development. Developed charge regulator determines the maximum power point by measuring the current voltage values and charge the battery at this point. The current, voltage, battery charge rate and charge status information obtained from the charge controller can be monitored by android mobile devices. The developed charge control circuit has been tested using a 45 Watt photovoltaic panel under different weather conditions. It was observed that it followed the maximum power point.