Maximum Power Point Tracking Problem Research Articles

Quantum maximum power point tracking (QMPPT) for optimal solar energy extraction

Solar energy is key to achieving a more environmentally responsible future. One way to exploit it is to use semiconductor technology through solar panels to generate clean, sustainable, and controllable energy. However, the use of such solutions must be optimised by methods such as maximum power point tracking (MPPT) to extract the maximum available solar energy. Although MPPT algorithms have been widely used and improved, the use of newer approaches, such as quantum computing, appears to hold the promise of achieving new performance levels, particularly for real-time MPPT implementation. The goal of this work is to develop and test a quantum algorithm for the photovoltaic (PV) energy MPPT problem using quantum particle swarm optimisation. The performance of the classic and quantum MPPT algorithms was evaluated under three main operating conditions: normal, high-temperature, and partial shading conditions. This represents a variety of environmental scenarios that can affect the efficiency of solar power generation. According to the study's results, the classical algorithm recorded 0.15% more power than the quantum algorithm in normal operating conditions, and the quantum algorithm generated 3.33% more power in higher temperature tests and 0.89% more power in the partial shading test. Moreover, the quantum algorithm recorded lower duty cycles for the three tests. While the classical algorithm may have a slight edge in power output under normal operation conditions, the quantum algorithm indicates superior performance in challenging conditions and consistently reveals more promising overall efficiency.

Comment on “Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids”

In a recent work, (Avila et al., 2020), an environment was developed and reported for partial shading conditions (PSC) in the open-source OpenAI Gym platform. This work presented deep reinforcement learning (DRL) techniques to address the maximum power point tracking (MPPT) problem of a photovoltaic (PV) array under PSC. Two DRL algorithms, namely, Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3) were investigated. A deviation of less than 1%, compared to the theoretical maximum power, was claimed for the DDPG algorithm. Based on the presented fact-based investigations, this comment highlights the issues in the reported approach. The main issues are found to be the approximate PV array modeling, erroneous performance metric and erroneous choice of DRL algorithms. Of the presented PSCs, the proposed technique in Avila et al. (2020) always attained the very first peak of the PV characteristic of the PV system. It may be noted that the first peak may not always be the global maximum power point. Overall, based on the presented investigations this comment demonstrates that the DDPG technique used in Avila et al. (2020) is not able to effectively address the MPPT problem in PV array under PSC.

A Reinforcement Learning approach to the Optimal Torque MPPT problem in wind turbines

In this work, a torque controller for a variable rotational speed wind turbine has been modelled using Reinforcement Learning and considering the Optimal Torque - Maximum Power Point Tracking problem as one of optimization. The reward optimization function is designed as a non-linear function depending mainly on the rotor power variation. Based on this, an optimal action (electromagnetic torque variation) regulates the turbine rotational speed. A simulated 1.5 MW three bladed wind turbine operation is managed by the torque controller. It keeps the turbine working at optimal operational conditions after a successful training process, which is carried out using the Proximal Policy Optimization algorithm. For the controller training, the turbine confronts constant and then randomly staggered wind speed behaviour. Time series of rotor angular speed, torque and power are presented. Our results show that the modelled controller is able to reach and maintain the wind turbine operation at its optimal power generation conditions. This methodology avoids using some empirical parameter characteristic of the Optimal Torque - Maximum Power Point Tracking algorithm widely used in wind turbine control systems.

A bio inspired maximum power point tracking controller for PV systems under partial shading conditions

Maximum power point tracking (MPPT) is a technique used in extracting the maximum power from a photovoltaic panel (PV) under different weather conditions. The last decade has witnessed a wide variety of algorithm based on MPPT controllers, ranging from simple to more complexe ones. Each of them has its own advantage and disadvantage. Hence, it is crutial to propose methods that are both simple and effective to track and maintain the MPPT of a PV system, even under partial shading conditions. In this study, we propose a new bio inspired method namely seagull optimization algorithm (SOA) for solving the MPPT problem in a PV system. To evaluate the proposed SOA _MPPT performance in terms of accuracy, convergence and stability, a simulation methodology is used. First, by tunning the appropriate parameters, then, we consider the following scenarios: rapid change of solar irradiation, temperature, and three patterns to test partial shading effect. The results are compared with latest bio-inspired methods, namely, particle swarm optimization (PSO), Bat optimisation algorithm (BAT) and fire fly algorithm (FA). The obtained results confirm the effectiveness and robustness of the proposed controller compared to existing conventional and bio inspired controllers.

BOSS-D-RBFN: BOosted Salp Swarm optimization based Deep RBFN for MPPT under partial shading condition in photovoltaic systems

PhotoVoltaic (PV) systems play a promising role in renewable energy resources (REs) as it provides environmentally friendly energy without pollution. PV systems under Partial Shading conditions (PSc) can significantly drop off their productivity of power where the arrays are shaded for various reasons, which lead to the output power that has many peaks, so there is a chance for local minima. Maximum Power Point Tracking (MPPT) methods will reduce the loss created over the PSc. Even though many conventional and soft computing approaches are commonly employed for the MPPT problem, conventional approaches show inadequate efficiency owing to fixed step size, and most of the existing soft computing approaches are limited by complicated rules implementation and require reinitialization during changing environmental conditions. So in this work, Deep learning based Radial Basis Function Network (D-RBFN) is proposed for the MPPT method as this neural network does not oscillate nearby the maximum power point region and performs well in nonlinear and rapid changing condition. Also, RBFN is optimized with the proposed BOosted Salp Swarm optimization (BOSS) approach to attaining higher accuracy and convergence speed by removing the random weights. Moreover, this proposed BOSS-D-RBFN method uses a DC–DC boost converter for faster transient response. The proposed method is evaluated against the existing classical MPPT, neural network-based MPPT, and fuzzy logic-based MPPT method to validate the performance. The results from the simulation study proved that our proposed BOSS-D-RBFN performs better compared to other existing methods with respect to global MPPT and MPPT power efficiency.

A Reduced Search Space Exploration Metaheuristic Algorithm for MPPT

The necessity for clean and sustainable energy has shifted the energy sector’s interest in renewable energy sources. Photovoltaics (PV) is the most popular renewable energy source because the sun is ubiquitous. However, PV’s power transfer efficiency varies with different load’s electrical characteristics, temperatures on PV panels, and insolation conditions. Based on these factors, Maximum Power Point Tracking (MPPT) is a mechanism formulated as an optimization problem adjusting the PV to deliver the maximum power to the load. Under full insolation conditions, varying solar panel temperatures, and different loads MPPT problem is a convex optimization problem. However, when the PV’s surface is partially shaded, multiple power peaks are created in the power versus voltage (P-V) curve making MPPT non-convex. Unfortunately, all optimization strategies for MPPT under partial shading applied in previous works, from traditional techniques to Machine Learning and the recently proposed Nature-inspired algorithms, were either computationally expensive or/and led to extensive power losses. To this end, this work presents an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking. Our experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses during tracking compared to two very recently proposed MPPT algorithms under partial shading conditions.

A Novel Maximum Power Point Tracking Strategy Based on Enhanced Real-Time Adaptive Step-Size Modified Control for Photovoltaic Systems

With the development of society, the demand for energy keeps increasing. Solar energy has received widespread concern for its renewable and environmentally friendly advantages. As one of the most efficient solar energy devices, the output power of photovoltaic (PV) cells is easily affected by the external environment. In order to solve the problem of the maximum power output of PV cells, this paper proposed a maximum power point tracking (MPPT) method. Based on the online particle swarm optimization (PSO) variable step length algorithm, the pulse width modulation (PWM) control module parameters are set according to the parameters of the PV cells’ output voltage. By dynamically adjusting the output voltage step of the PV cells online, the output of the PV cells is stabilized near the maximum power point (MPP). The simulation results concluded that the method and model could accurately adjust the output voltage according to the external environment changes in real time and reduce the voltage fluctuation at the MPP, providing a new idea to solve the problem of MPPT of PV cells.

Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids

Photovoltaic systems (PV) are having an increased importance in modern smart grids systems. Usually, in order to maximize the energy output of the PV arrays a maximum power point tracking (MPPT) algorithm is used. However, once deployed, weather conditions such as clouds can cause shades in the PV arrays affecting the dynamics of each panel differently. These conditions directly affect the available energy output of the arrays and in turn make the MPPT task extremely difficult. For these reasons, under partial shading conditions, it is necessary to have algorithms that are able to learn and adapt online to the changing state of the system. In this work we propose the use of deep reinforcement learning (DRL) techniques to address the MPPT problem of a PV array under partial shading conditions. We develop a model free RL algorithm to maximize the efficiency in MPPT control. The agent’s policy is parameterized by neural networks, which take the sensory information as input and directly output the control signal. Furthermore, a PV environment under shading conditions was developed in the open source OpenAI Gym platform and is made available in an open repository. Several tests are performed, using the developed simulated environment, to test the robustness of the proposed control strategies to different climate conditions. The obtained results show the feasibility of our proposal with a successful performance with fast responses and stable behaviors. The best results for the presented methodology show that the maximum operating power point achieved has a deviation less than 1% compared to the theoretical maximum power point.

An Adaptive Model Predictive Controller for Current Sensorless MPPT in PV Systems

Finite control set model predictive control (MPC) is a model-based control method that can include multi-objective optimization, constrained control, adaptive control, and online auto-tuning of weighting factors all in a single controller that exhibits fast dynamic tracking. This paper utilizes the model-based framework of MPC to develop a sensorless current maximum power point tracking (MPPT) algorithm. Eliminating the current sensor can reduce the cost and improve the reliability of the photovoltaic system. This paper also utilizes constrained control and online auto-tuning of MPC to develop an adaptive perturbation MPPT to reduce steady-state oscillation and improve dynamic performance. This paper builds in a single framework the different layers of the MPPT problem: control, estimation, and MPPT. The proposed adaptive perturbation sensorless current mode MPPT (ASC-MPPT) technique performance is compared to the well-known incremental conductance (InCon) MPPT technique. The EN50530 European industrial test standards were used to demonstrate performance.

Cuckoo Search Based Approach Towards Maximum Power Point Tracking for Solar Photovoltaic System Under Partial Shading

Extracting the maximum power from as solar PV system is a critical task when high changes in light intensity or Partial Shading Condition (PSC) are experienced. The latter case is more difficult as it creates multiple maxima points on P–V curve. In this way, it is obligatory to thoroughly pick a precise Maximum Power Point Tracking (MPPT) method which recognizes adequately the Global Maximum Power Point (GMPP) and tracks it under partial shading. This paper first describes the modeling of PV module and PV characteristics under uniform irradiance as well as effect of PSC on PV characteristics. In the latter sections, a review of conventional and intelligent MPPT methods is done. To tackle the problem of MPPT under PSC, two metaheurisric algorithms namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are described briefly. A new optimization method called Cuckoo Search (CS) is implemented in MATLAB SIMULINK tool and tested under three different PSC patterns. A comparative analysis of different MPPT strategies is made after analyzing the results.

Active disturbance rejection based MPPT control for wind energy conversion system under uncertain wind velocity changes

The tip speed ratio regulation based maximum power point tracking (MPPT) control for the wind energy conversion system (WECS) is studied in this paper. Challenges imposed by uncertain changes in wind velocity and unmodelled system dynamics are considered. A first order active disturbance rejection controller (ADRC) is designed to tackle this problem. Based on the platform of the high-power (2-MW) rigid-drive-train squirrel-cage induction generator-based wind energy conversion system (WECS), a simulation study is carried out by comparing with the performance of a well-tuned published PI (proportional–integral) speed controller, and the results show that the proposed first order ADRC controller exhibits better tracking performance and adaptability for uncertain wind velocity changes. There are only two controller parameters, namely, ωo and ωc, to tune, and control adaptability can be held without parameters resetting or extractable mechanical power reducing, proving the effectiveness of the proposed ADRC approach for the WECS MPPT problem.

Configuration of marine photovoltaic system and its MPPT using model predictive control

Due to the worldwide energy crisis and environmental issues, the so-called green ship is recently urgent to be developed for energy-saving and emission-reduction. When a photovoltaic (PV) system is installed on a ship, solar generation can provide auxiliary energy as backup of engine. However, different from the land-based PV system with fixed position, the marine PV system always suffers from complex and frequently-changed environmental conditions, e.g. the partial and dynamic shading, so a maximum power point tracking (MPPT) approach with higher accuracy and faster response is required. In this paper, a novel configuration of large-scale PV array on green ocean-going ship is studied, and its MPPT problem is described as a large-scale optimization model. Then, a meta-heuristic optimization is employed to solve this model offline, and the model predictive control is employed to achieve the online MPPT control in real-time. Result of simulation experiments shows that the proposed configuration and MPPT approach can achieve effective performance, and also can improve the output power under complex environmental conditions significantly.

A Novel Flower Pollination Based Global Maximum Power Point Method for Solar Maximum Power Point Tracking

To maximize solar photovoltaic (PV) output under dynamic weather conditions, maximum power point tracking (MPPT) controllers are incorporated in solar PV systems. However, the occurrence of multiple peaks due to partial shading adds complexity to the tracking process. Even though conventional and soft computing techniques are widely used to solve MPPT problem, conventional methods exhibit limited performance due to fixed step size, whereas soft computing techniques are restricted by insufficient randomness after reaching the vicinity of maximum power. Hence, in this paper, a new flower pollination algorithm (FPA) with the ability to reach global peak is proposed. Optimization process in FPA method performs global and local search in single stage and it is a key tool for its success in MPPT application. The ruggedness of the algorithm is tested with zero, weak, and strong shade pattern. Further, comprehensive performance estimation via simulation and hardware are carried out for FPA method and are quantified with conventional perturb and observe and particle swarm optimization (PSO) methods. Results obtained with FPA method show superiority in energy saving and proved to be economical.

Peak power detection of PS solar PV panel by using WPSCO

This study introduces a mathematical trajectory based ‘Weibull Pareto sine–cosine optimisation (WPSCO)’ algorithm for the maximum power point tracking (MPPT) in the dynamics as well as in steady-state conditions of a partially shaded (PS) solar photovoltaic (PV) system. This ‘WPSCO’ technique is used for quick and oscillation-free tracking of the global best peak position in a very less number of steps, which is necessary to work in real-time atmospheric conditions. The unique advantage of this algorithm for MPPT problem in PS condition is as it is free from common and generalised problem of the other evolutionary techniques such as longer convergence duration, a large number of search particles, steady-state oscillation, unnecessary computational burden etc., which create power loss and oscillations in output. This hybrid algorithm is tested on different types of PV characteristics of the PS solar PV array by using MATLAB simulator and verified on a developed hardware of the solar PV system. Moreover, the tracking ability is compared with the state-of-the-art methods. The satisfactory steady-state and dynamic performances of the WPSCO algorithm under variable irradiance and temperature levels show the superiority over the state-of-the-art control methods.

PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load

Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV---DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by artificial bee colony (ABC) to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with genetic algorithm for various disturbances to prove its robustness.

PI controller design using artificial bee colony algorithm for MPPT of photovoltaic system supplied DC motor‐pump load

Maximum Power Point Tracking (MPPT) is used in Photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV‐DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by Artificial Bee Colony (ABC) to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with Genetic Algorithm for various disturbances to prove its robustness. © 2015 Wiley Periodicals, Inc. Complexity 21: 99–111, 2016

Maximum Power Point Tracking for Photovoltaic System Using Adaptive Extremum Seeking Control

In order for photovoltaic (PV) systems to maximize their efficiency of power generation, it is crucial to locate the maximum power point (MPP) in real time under realistic illumination conditions. The current-voltage (I-V) characteristics of PV devices are nonlinear, and the MPP may vary with intrinsic and environmental conditions. Maximum power point tracking (MPPT) control is expected to seek the MPP regardless of the device and ambient changes. This brief presents the application of the adaptive extremum seeking control (AESC) scheme to the PV MPPT problem. A state-space model is derived via averaging method, with the control input being the duty ratio of the pulse-width modulator of the dc-dc buck converter. To address the nonlinear PV characteristics, the radial basis function neural network is used to approximate the unknown nonlinear (I-V) curve. The convergence of the system to an adjustable neighborhood of the optimum is guaranteed by utilizing a Lyapunov-based adaptive control method. The performance of the AESC is verified with simulation.

A meta-heuristics search algorithm as a solution for energy transfer maximization in stand-alone photovoltaic systems

This paper studies the problem of MPPT (maximum power point tracking) for photovoltaic systems. The paper offers new solution framework for the above mentioned popular problem in two senses. Firstly a novel MPPT technique is presented. The main idea behind it is to improve the computational efficiency of the traditional incremental conductance tracking algorithm. This is achieved by integration with the fractional open circuit voltage method leading to fast and accurate convergence to the MPP. Secondly, with the aid of certain class of meta-heuristics search algorithms – inspired from the real ant behavior – the MPPT problem is amenable to solution for two different and well known control schemes namely incremental conductance plus proportional integral controller and fuzzy control. A comparison with conventional approach reflects the superiority of the new framework even under fast changing climatic conditions.