Sudden Changes In Irradiance Research Articles

Research on Hybrid Approach for Maximum Power Point Tracking of Photovoltaic Systems under Various Operating Conditions

Based on the characteristics of the whale optimization algorithm (WOA) and perturbation observation (P&O) method, this paper proposes a novel hybrid approach called the improved chaotic whale optimization combined with perturb and observe (ICWOA-P&O) method for maximum power point tracking (MPPT) control to solve the challenge of low efficiency in photovoltaic (PV) power generation under local shadows. First, the ICWOA is used for a global search to quickly locate the position of the maximum power point (MPP). Then, the P&O method is used for a fine-grained local search to quickly track the position of the global maximum power point (GMPP) with low oscillation. To ensure accuracy, the tracking performance of the ICWOA-P&O method is comprehensively compared with the WOA-P&O, WOA, and PSO models under four conditions: uniform irradiance, static local shading, dynamic shading, and sudden changes in irradiance and temperature. The simulation results verify that under the above four conditions, the ICWOA-P&O method can track the MPP continuously and stably and greatly improves the convergence time and accuracy. Compared with the other three methods, the ICWOA-P&O method can effectively obtain the fastest tracking speed (less than 0.1 s), the highest tracking accuracy (more than 99.97%), the smallest relative error (less than 0.03%), and the smallest oscillation fluctuation. Finally, this study integrated the ICWOA-P&O algorithm into the designed MPPT controller hardware and established a practical PV experimental platform based on the ICWOA-P&O control algorithm for practical tests.

Design of PV, Battery, and Supercapacitor-Based Bidirectional DC-DC Converter Using Fuzzy Logic Controller for HESS in DC Microgrid

Renewable energy sources (RES) are becoming more popular globally as a reaction to critical energy concerns. Modern energy management technologies are used to maximize their efficiency while preserving the reliability of the grid. A hybrid energy storage system (HESS) connects to the DC microgrid through the bidirectional converter, allowing energy to be transferred among the battery and supercapacitor (SC). In this paper, a fuzzy logic control (FLC) technique is developed for PV-based DC microgrid systems that use both batteries and SCs. The proposed method uses the unbalanced energy from the battery pack to enhance the overall effectiveness of the HESS. The FLC approach is performed to validate under conditions of variable irradiance using MATLAB Simulink. When sudden changes in irradiance occur, the proposed FLC brings the voltage back to the desired level in terms of transient response like 33 ms settling times and 19% overshoot values. The results exhibit that the proposed method is more efficient in terms of time response, power output, increasing battery life, and ensuring a continuous supply of the PV system.

Data-Driven Switch Fault Diagnosis for DC/DC Boost Converters in Photovoltaic Applications

Switch semiconductors are essential components in power electronics stages for photovoltaic (PV) systems, which are prone to failures caused mainly by thermal stress, thereby affecting PV energy production. Specifically, dc/dc boost power converters are commonly used as maximum power point tracking (MPPT) systems. Several works have been reported so far in the literature for switch fault detection and isolation (FDI) purposes in boost power converters. However, most of them require additional sensors and circuitry to those included in any PV system, which generate extra costs on the overall system. Hence, this work presents an efficient and low-cost switch fault diagnosis proposal, which requires only the most common measurements in PV systems, namely, PV current and voltage. This work departs from a data-driven FDI methodology for diagnosing switch open- and short-circuit faults in dc/dc boost converters. The experimental results have been evaluated against sudden irradiance changes and switch faults in a test bench of 350 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm {W}$</tex-math></inline-formula> operating under a closed-loop nonlinear control action for MPPT purposes by using the dSpace 1104 board.

Novel MPPT Controller Augmented with Neural Network for Use with Photovoltaic Systems Experiencing Rapid Solar Radiation Changes

A maximum power point tracking (MPPT) controller optimizes power harvesting in photovoltaic (PV) systems under varying conditions. The perturb and observation (P&amp;O) algorithm is commonly used for MPP tracking, but suffers from slow response, loss of tracking direction, and entrapment. The current research proposes a neural network (NN) integrated with the P&amp;O algorithm to enhance tracking performance during sudden variations in solar irradiance. The proposed neural network updates the duty cycle change when detecting sudden changes. It effectively estimates the duty cycle change even when trained with a small dataset. The integration between the NN and P&amp;O significantly improves tracking performance compared with the conventional P&amp;O algorithm, especially under sudden irradiance changes.

An accurate real time neural network based irradiance and temperature sensor for photovoltaic applications

The functioning of photovoltaic (PV) systems mainly depends on the climatic conditions, including incident solar irradiance intensity and module temperature. Thus, irradiance and operating temperature are key factors for assessing and monitoring the performance of PV systems. This work develops a PV solar cells-based sensor for accurate measurement of both incident irradiance and the PV operating temperature. The proposed sensor relies on a Neural Network (NN) soft sensor, trained to estimate PV modules incident solar irradiance and operating temperature without direct measurements. The trained NN model uses the short circuit current (ISC) and open-circuit voltage (VOC) of a reference PV module as inputs of the estimation. The accuracy of the proposed sensor is validated through indoor and outdoor experiments using a PV emulator and a real panel, respectively. Indoor results affirmed the accurate tracking capability of the proposed estimator for both irradiance and module temperature, in constant and rapidly changing conditions, including sudden irradiance changes. The estimator achieved an exceptional level of performance with a Mean Absolute Percentage Error (MAPE) of 1.45% for irradiance and 1.64% for temperature. Similarly, the outdoor experiments using real-time data further validate the obtained results with a Normalized Root Mean Square Error (NRMSE) of 1.22% for irradiance and 3.50% for temperature, outperforming other methods published in the literature.

Centralized MPPT based on Sliding Mode Control and XBee 900 MHz for PV systems

Maximum Power Point Tracking (MPPT) algorithms are essential to achieve the best overall performance of PV systems. Typically, MPPT techniques are implemented over wired communications, which connect sensors, computing nodes and DC–DC converters. However, the introduction of wireless communication systems can offer more flexibility for information collection, dissemination, and processing when compared with wired communication infrastructures. This article proposes a wireless control architecture that allows the execution of the MPPT algorithms for several PV subsystems on a central node with limited computing power. This approach introduces flexibility and optimizes energy production at PV systems. The presented approach uses Current and Voltage measures, as well as the Irradiance and Cell Temperature values obtained at the PV panel, to calculate the optimal PWM duty cycle for the DC–DC converter which extracts the maximum power from the PV modules. Wireless communications are implemented by means of XBee 900 MHz RF modules. These modules provide a low cost solution with a relatively long range communication, avoiding the overpopulated 2.4 GHz band, in which other common technologies may cause interferences. Although the proposed approach allows the use of different control algorithms, Sliding Mode Control (SMC) techniques were selected, since this control technique ensures robustness and low computational complexity. The proposed SMC was simulated and compared with other conventional techniques with low computational cost, namely PID and P&O. Simulation results proved that SMC provides better performance and higher robustness when sudden changes in irradiance occur. The presented approach was experimentally tested in a PV rooftop installation at the Faculty of Engineering of Vitoria-Gasteiz. The obtained results suggest that it may be a valid way of maximizing energy production in PV systems while avoiding the constraints of wired communications.

Asymmetrical interval Type-2 Fuzzy logic controller based MPPT for PV system under sudden irradiance changes

Asymmetrical interval Type-2 Fuzzy logic controller based MPPT for PV system under sudden irradiance changes

Experimental Modeling of a New Multi-Degree-of-Freedom Fuzzy Controller Based Maximum Power Point Tracking from a Photovoltaic System

Conventional control methods, which follow the maximum power point (MPP), suffer from being slow or inaccurate during sudden changes in irradiance and temperature. These problems can be solved using artificial intelligence algorithms. The current study proposes a new multi-degree-of-freedom (MDOF) fuzzy logic controller (FLC) for maximizing the overall output performance of a photovoltaic system. The MDOF-FLC was compared to the simplified universal intelligent PID controller (SUI-PID) using the MDOF concept and the normal FLC. Simulation and experimental results show that the proposed MDOF-FLC controller has a 37.8% and 58.1% faster response with a better rise time compared to the SUIPID controller and the normal FLC, respectively. At the same time, the error, measured by the integral time absolute error (ITAE), was 29.4% and 62.5% lower, respectively.

System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles

The integration of photovoltaic systems (PVS) in electric vehicles (EV) increases the vehicle’s autonomy by providing an additional energy source other than the battery. However, current solar cell technology generates around 200 W for a 1.4 m2 panel (to be installed on the roof of the EV) at stable irradiance conditions. This limitation in production and the sudden changes in irradiance produced by shadows of clouds, buildings, and other structures make developing a fast and efficient maximum power point tracking (MPPT) technique in this area necessary. This article proposes an artificial neural network (ANN)-based MPPT, called DS-ANN, that uses manufacturer datasheet parameters as inputs to the network to address this problem. The Bayesian backpropagation-regularization performs the training, ensuring that the MPPT technique operates satisfactorily on different PVS without retraining. We simulated the response of 20 commercial modules against actual irradiance data to validate the proposed method. The results show that our method achieves an average tracking efficiency of 99.66%, improving by 1.21% over an enhanced P&amp;O method.

A novel fuzzy controller for photovoltaic pumping systems driven by general-purpose frequency converters

In this paper, a fuzzy logic based Mandani-type controller is applied to an industrial frequency converter to couple a photovoltaic generator to a conventional centrifugal pump for water pumping applications. The system operates in closed loop and the DC bus voltage regulation of the frequency converter is done indirectly by varying the speed of the pump according to the power provided by the photovoltaic generator. In order to decrease the number of series-connected modules, the working voltage of the photovoltaic generator (maximum power point voltage) must be as close as possible to the minimum voltage limit required by the frequency converter’s input. The proposed algorithm was embedded in a general-purpose microcontroller. Controller performance was evaluated through tests on an experimental bench that allows simulating different water heads. Experimental results shows that the speed of the proposed fuzzy controller allows the operation close to the lower DC voltage limit imposed by the frequency converter, making possible the use of photovoltaic generators with a reduced number of PV modules in series (in this work, 30% fewer modules). The fuzzy controller regulates the photovoltaic generator voltage throughout the day, avoiding frequency converter limitation due to undervoltage errors, even with sudden changes in irradiance.

A Predictive Control Scheme for Large-Scale Grid-Connected PV System Using High-Level NPC Inverter

This paper focuses on the control of large-scale grid-connected photovoltaic system. The proposed system is composed of two conversion stages: the first stage contains four PV arrays, each one connected to an individual DC/DC converter (boost converter), while the second stage is a five-level neutral point clamped (NPC) inverter tied to the grid. Each DC-link capacitor input of the NPC inverter is connected to the output of the DC/DC boost converter. In order to enhance the energy harvesting capability of the proposed system, different controllers based on finite-set model predictive control (FS-MPC) are developed and presented. A fast voltage-oriented maximum power point tracking performed by FS-MP current controller (FS-MPCC) is investigated and applied for each boost converter to maximize the produced power from each PV array. Furthermore, a FS-MPC algorithm is proposed to control the centralized five-level NPC inverter. The purposes of the developed controllers are: track the MPP rapidly and accurately under sudden irradiation changes, ensure the balance of the four DC-link capacitor voltages whatever the difference between the extracted powers from each PV system unit, inject the reactive power demanded by the grid operator and also minimize the switching frequency of the five-level NPC inverter. The results obtained through MATLAB/Simulink and Simpower toolbox packages prove that the proposed control scheme provides better performance in comparison with conventional control scheme. Moreover, a high grid current quality and perfect DC-link capacitor voltages balancing are ensured under contrast of extract powers from each PV system.

DC-DC Boost Converter Design for Fast and Accurate MPPT Algorithms in Stand-Alone Photovoltaic System

&lt;span&gt;The main components of a Stand-Alone Photovoltaic (SAPV) system consists of PV array, DC-DC converter, load and the maximum power point tracking (MPPT) control algorithm. MPPT algorithm was used for extracting maximum available power from PV module under a particular environmental condition by controlling the duty ratio of DC-DC converter. Based on maximum power transfer theorem, by changing the duty cycle, the load resistance as seen by the source is varied and matched with the internal resistance of PV module at maximum power point (MPP) so as to transfer the maximum power. Under sudden changes in solar irradiance, the selection of MPPT algorithm’s sampling time (T&lt;sub&gt;S_MPPT&lt;/sub&gt;) is very much depends on two main components of the converter circuit namely; inductor and capacitor. As the value of these components increases, the settling time of the transient response for PV voltage and current will also increase linearly. Consequently, T&lt;sub&gt;S_MPPT &lt;/sub&gt;needs to be increased for accurate MPPT and therefore reduce the tracking speed. This work presents a design considerations of DC-DC Boost Converter used in SAPV system for fast and accurate MPPT algorithm. The conventional Hill Climbing (HC) algorithm has been applied to track the MPP when subjected to sudden changes in solar irradiance. By selecting the optimum value of the converter circuit components, a fast and accurate MPPT especially during sudden changes in irradiance has been realized.&lt;/span&gt;

A New Approach to Maximum Power Point Tracking for Sudden Changes in Irradiance in Photovoltaic Systems

Background/Objective: This paper presents a new digital control approach to track the Maximum Power Point (MPPT) for Potovoltaic (PV) systems. A photovoltaic power generation system requires an effective controller to overcome sudden changes to irradiance and to maximize its efficiency. Methods/Statistical Analysis: A new approach to MPPT based on voltage control was proposed, while a PI (Proportional Integral) controller was combined with the boost converter to adapt the duty cycle. Findings: The input voltage reference was adaptively perturbed with variable steps until the maximum power was reached. A state-space model was derived through the averaging method, with the control input being the duty ratio to regulate the pulse width modulator on the DC-DC boost converter. The proposed control scheme eliminated steady state oscillations around the working point area of the PV panel when there were rapid changes to the irradiance. Applications/Improvements: Furthermore, the PV system became more efficient, as proven by the sudden change in the radiation conditions for 0.3 seconds, where approximately 30% of energy could be saved. Keywords: Averaging Model, Boost Converter, Combination IP, Irradiance, New MPPT, Photovoltaic

A New Approach to Maximum Power Point Tracking for Sudden Changes in Irradiance in Photovoltaic Systems

A New Approach to Maximum Power Point Tracking for Sudden Changes in Irradiance in Photovoltaic Systems

A high-performance control scheme for photovoltaic pumping system under sudden irradiance and load changes

A low-cost photovoltaic (PV) pumping system based on three phase induction motor (IM) without the use of chemical energy storage elements is presented in this paper. The PV generator-side boost converter performs the maximum power point tracking (MPPT), while the IM−side two-level inverter regulates the net DC-link voltage and the developed electromagnetic torque by IM, which is coupled with a centrifugal pump. An improved variable step size perturb and observe (P&O) algorithm is proposed to reduce the steady-state PV power fluctuation, to accelerate the tracking operation under sudden irradiance changes, and to protect IM under load drops. The proposed algorithm is based on a current control approach of the boost converter with a model predictive current controller to select the optimal control action. Moreover, predictive torque and flux control (PTC) is used to control IM drive, due to its advantages such as faster torque response, lower torque ripple, and simplicity of implementation. Furthermore, a Takagi-Sugeno (T-S) type fuzzy logic controller (FLC) is developed in order to regulate the DC-link voltage, by producing the torque reference for PTC algorithm. In order to examine and assess the performance of the proposed control scheme for PV pumping system, a complete simulation model is developed using MATLAB/SimulinkTM environment and confirmed through real-time hardware in the loop (HIL) system. The obtained results indicate the excellent performance of the proposed control scheme, which is much better than the conventional scheme based on conventional techniques (P&O algorithm and direct torque control (DTC)).

A proposed advanced maximum power point tracking control for a photovoltaic-solar pump system

The maximum power point tracking (MPPT) for a photovoltaic (PV) system using the conventional methods suffers from being slow or inaccurate during sudden changes in irradiance and temperature. Artificial intelligent algorithms can overcome these drawbacks. However, these algorithms have high complexity in the design and implementation. Thus, the present work applies a Simplified Universal Intelligent PID Controller (SUI-PID) to extract the maximum power from a photovoltaic system with a solar pump as the load. The proposed SUI-PID controller was compared to the Fuzzy logic controller (FLC) under different operating conditions. The MATLAB/Simulink software package was utilized for the system simulation. Simulation results show that the proposed SUI-PID controller has 32.7% faster response with better rise time compare to the FLC. The SUI-PID controller offers additional advantages like the simplicity in design and implementation compared to other intelligent algorithms.

Conventional and modified MPPT techniques with direct control and dual scaled adaptive step-size

Conventionally, for adaptive MPPT change in power over change of voltage (dPPV/dVPV), change in power over change of current (dPPV/dIPV) or change in power over change of duty (dPPV/dD) had been utilized for variable stepping. However, this paper presents dual scaled adaptive step sizing, depending on change of power (dPPV). This technique reduces complexity, improves transient and dynamic response to sudden irradiance changes. As, PV characteristic curve has two different slopes, one at the left and the other at the right of MPP. Nevertheless, a constant fine-tuned scaling factor for operation at left of MPP may cause overshoot or undershoot at right side of MPP. Similarly, constant scaling factor offering good performance at right slope of the MPP may give slow voltage response when the system operates at left of MPP. Therefore, dual scale adaptive technique dependent on change of power (dPPV) has been proposed to attain credible performance at both sides of the slope. This technique has been employed on conventional and modified MPPT schemes with direct control, explicitly on modified Incremental Conductance (IncCond) method. Further, the aim is to establish the fact that even with direct implementation, IncCond remains a specific implementation of P&O, by comparatively analyzing the performance of each method. Simulation and experimental results authenticate the validity of the proposed scheme.

Novel control algorithm for MPPT with Boost converters in photovoltaic systems

The great advances in efficiency and performance of photovoltaic modules would not be very useful if they do not work close to their maximum power point (MPP). In this paper a novel Sliding Mode Control (SMC) based algorithm is proposed to be implemented in a DC/DC converter in order to make an autonomous photovoltaic system to work at the MPP. Once that the design of the novel algorithm has been detailed (especially the novel part relative to the current reference signal) and its stability has been demonstrated, its performance has been compared with two of the most commonly used algorithms in this scope, i.e., Perturbation & Observation (P&O) and Incremental Conductance (IC) algorithms, in addition to a PI controller because it is one of the preferred controllers in industrial applications. This comparison has been carried out taking into account both simulated and experimental tests. The first focused on their behavior when sudden changes in irradiance and temperature, while the lasts analyzed them when the load resistance was varying arbitrarily in actual facilities (composed of a photovoltaic module Mitsubishi PV-TD185MF5, a Boost converter, a variable load and a real-time data acquisition card dSPACE DSP1104 used as the interface between the control algorithm implemented in Simulink/Matlab and the real photovoltaic module). After completing tests under different conditions, we found that the proposed SMC based algorithm outperforms the PI controller and the P&O and IC algorithms, especially in experiments carried out using actual facilities.

Load Controlled Adaptive P&O MPPTController PV Energy Systems

A adaptive pert urb and observe (P&O) maximum power point tracking (MPPT) algorithm is proposed to track the maximum power under sudden changes in irradiance. The proposed method consists of three algorithms, namely current perturbation algorithm (CPA), adaptive control algorithm (ACA), and variable perturbation algorithm (VPA). CPA always tries to operate the photovoltaic (PV panel at maximum power point (MPP). ACA sets the operating point closer to MPP, only if the operating limits are violated. These operating limits are expressed in terms of the operating current range of the PV panel and the sudden changes in irradiance. The perturbation size based on polarity of change in power.VPA is dynamically reduced.

Variable Perturbation Size Adaptive P&amp;amp;O MPPT Algorithm for Sudden Changes in Irradiance

In this paper, a variable perturbation size adaptive perturb and observe (P&O) maximum power point tracking (MPPT) algorithm is proposed to track the maximum power under sudden changes in irradiance. The proposed method consists of three algorithms, namely current perturbation algorithm (CPA), adaptive control algorithm (ACA), and variable perturbation algorithm (VPA). CPA always tries to operate the photovoltaic (PV) panel at maximum power point (MPP). ACA sets the operating point closer to MPP, only if the operating limits are violated. These operating limits are expressed in terms of the operating current range of the PV panel and the sudden changes in irradiance. VPA dynamically reduces the perturbation size based on polarity of change in power. Two-stage variable size perturbation is proposed in this paper. The proposed algorithm is realized using a boost converter. The effectiveness of proposed algorithm in terms of dynamic performance and improved stability is validated by detailed simulation and experimental studies.