Photovoltaic Emulator Research Articles

Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems

This study introduces a novel approach for analyzing photovoltaic (PV) systems that employ block lookup tables for speedy and efficient simulation. It introduces an innovative method for tracking the Global Maximum Power Point (GMPP) by utilizing Zebra Optimization Algorithm (ZOA). The suggested method was carefully evaluated under difficult Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its global and local search capability. ZOA’s performance was examined in four scenarios and compared to four existing MPPT algorithms: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). ZOA surpassed its competitors with an average tracking time of 0.875 s and a tracking efficiency of 99.95% in PSCs. In comparison, ZOA increased tracking efficiency by up to 2%, increased resilience under varied circumstances, and produced a faster convergence speed—approaching the maximum Power Point 10–15% faster than the other algorithms. Furthermore, ZOA significantly decreased operating point variations. The algorithm’s overall performance was tested using an experimental setup with a DSPACE board and a PV emulator. These findings demonstrate that ZOA is a highly efficient and dependable MPPT solution for PV systems, especially in severe PSCs.

A Versatile Platform for PV System Integration into Microgrids

Advancing decarbonization critically depends on the integration of PV systems into microgrids. However, this integration faces challenges, including the variability of photovoltaic solar energy production, the demands of energy management, and the complexities of grid synchronization and communication. To address these challenges, a PV emulator platform is an essential tool. This paper presents a novel four-layer PV emulator platform that seamlessly integrates power systems, control systems, measurement instrumentation, and communication processes. The proposed platform enables the emulation of I-V curves and the dynamic adjustment of operating points—including both the maximum power point (MPP) and power reserve point (PRP)—as well as temperature and irradiance while providing sufficient power capacity for microgrid integration. To validate its effectiveness, the platform was assessed for its capability to adjust operating points, such as MPPs or PRPs, under varying irradiance and temperature conditions. The results show that the platform effectively adjusts operating points with a deviation of less than 5% from theoretical values and successfully tracks a sequence of operating points. This performance underscores the platform’s potential in integrating and managing PV systems within microgrid environments, thereby advancing the path to decarbonization.

Design, and dynamic evaluation of a novel photovoltaic pumping system emulation with DS1104 hardware setup: Towards innovative in green energy systems.

Diesel engines (DEs) commonly power pumps used in agricultural and grassland irrigation. However, relying on unpredictable and costly fuel sources for DEs pose's challenges related to availability, reliability, maintenance, and lifespan. Addressing these environmental concerns, this study introduces an emulation approach for photovoltaic (PV) water pumping (WP) systems. Emulation offers a promising alternative due to financial constraints, spatial limitations, and climate dependency in full-scale systems. The proposed setup includes three key elements: a PV system emulator employing back converter control to replicate PV panel characteristics, a boost converter with an MPPT algorithm for efficient power tracking across diverse conditions, and a motor pump (MP) emulator integrating an induction motor connected to a DC generator to simulate water pump behaviors. Precise induction motor control is achieved through a controlled inverter. This work innovatively combines PV and WP emulation while optimizing system dynamics, aiming to develop a comprehensive emulator and evaluate an enhanced control algorithm. An optimized scalar control strategy regulates the water MP, demonstrated through MATLAB/Simulink simulations that highlight superior performance and responsiveness to solar irradiation variations compared to conventional MPPT techniques. Experimental validation using the dSPACE control desk DS1104 confirms the emulator's ability to faithfully reproduce genuine solar panel characteristics.

Experimental validation of a novel hybrid Equilibrium Slime Mould Optimization for solar photovoltaic system

Maximizing Power Point Tracking (MPPT) is an essential technique in photovoltaic (PV) systems that guarantees the highest potential conversion of sunlight energy under any irradiance changes. Efficient and reliable MPPT technique is a challenge faced by researchers due to factors such as fluctuations in irradiance and the presence of partial shading. This paper introduced a novel hybrid Equilibrium Slime Mould Optimization (ESMO) MPPT-based algorithm combining the advantages of two recent algorithms, Slime Mould Optimization (SMO) and Equilibrium Optimizer (EO). The ESMO algorithm is compared with highly efficient MPPT-based techniques such as SMO, EO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA), both under a Simulink environment and a real-time experimental laboratory setup using a Dspace1104 controller and PV emulator. The comparison focuses on performance under several irradiance cases, including instant irradiance change, partial shading, complex partial shading, and dynamic partial shading. The key advantage of ESMO is the fact that it has a single tunable parameter, which makes implementation much easier and, at the same time, reduces the computational resources that are required by the control system. Extensive testing proves the superiority of ESMO over all other techniques, the average efficiency of which is 99.98% under all conditions. Additionally, ESMO provides fast average tracking times of 244 ms under simulation experiments and 200 ms for real-time experiments. These results show that ESMO can be very important for future implementation in large-scale solar PV systems.

Robust and fast emulation of PV panels using FSL algorithm and nonlinear power stage controller

The effective implementation of Photovoltaic (PV) systems relies heavily on controlled testing using Photovoltaic Emulator (PVE). This paper presents a novel PVE for rapid and robust testing of PV systems. A fifteen segments linearization (FSL) methodology is put-forward to resolve the nonlinear current-voltage (I-V) equations of PV modules. The I-V curve is precisely segmented into 15 linear elements, whose coordinates are used to construct a suitable reference voltage for the PVE. To ensure a robust and fast emulation of the solar panel, a nonlinear power stage controller (NPSC) is designed to maximize the Buck converter-power stage of the PVE. Several experiments have been performed under diverse environmental and load testing profiles, revealing a mean error and maximum error of less than 0.2 %, and less than 0.6 % respectively for three classes of commercial PV modules (multicrystal KC200GT, poly-crystalline MSX-60, and mono-crystalline CS6K-280 M). The high computation accuracy of the FSL position the proposed PVE as a simple but powerful emulation tool for PV systems. By evaluating its performance and comparing it to the state-of-the-art emulators, this paper aims to demonstrate the effectiveness and superiority of the FSL-NPSC-PVE in achieving rapid and robust emulation of different commercial solar panels.

ADJUSTABLE RESISTANCE FEEDBACK PIECEWISE LINEAR INTERPOLATION PHOTOVOLTAIC EMULATOR

Photovoltaic (PV) emulator (PVE) is a power converter that follows the voltage and current characteristic of a PV module. The direct referencing method (DRM) is the prevailing control strategy for the PVE because of its straightforward nature. Nonetheless, stability is an issue for this control strategy. The resistance feedback method (RFM) provides an alternative solution without encountering stability issues. The RFM requires a specialize current-resistance (I-R) PV model to operate. Currently, the adjustable piecewise linear interpolation (PLI) for this specialize PV model is unavailable for the RFM PVE. This paper proposed an adjustable I-R PLI (IRPLI) PV model for the PVE that uses the RFM control strategy. The performance of this PV model and its corresponding RFM PVE is compared with the current-voltage PLI (IVPLI) PV model together with the DRM PVE. The output of the IRPLI is similar to the IVPLI at various irradiance. The discrepancy in error for both PV models is a maximum of 6% when compared to the standard single diode model (SDM) PV model. The IRPLI is 3.2 times faster compared to the SDM and 1.07 times slower compared to the IVPLI. The IRPLI also able to properly integrated with the RFM PVM without any stability issues.

A New Fast and Efficient MPPT Algorithm for Partially Shaded PV Systems Using a Hyperbolic Slime Mould Algorithm

The design of new efficient maximum power point tracking (MPPT) techniques has become extremely important due to the rapid expansion of photovoltaic (PV) systems. Because under shading conditions the characteristics of PV devices become multimodal having several power peaks, traditional MPPT techniques provide crappy performance. In turn, metaheuristic algorithms have become massively employed as a typical substitute in maximum power point tracking. In this work, a new optimizer, which was named the hyperbolic slime mould algorithm (HSMA), is designed to be employed as an efficient MPPT algorithm. The hyperbolic tangent function is incorporated into the optimizer framework equations to scale down large perturbations in the tracking stage and boost its convergence trend. Moreover, to provide a strong exploration capability, a new mechanism has been developed in such a way the search process is carried out inside the best two power peak regions along the initial iterations. This region inspection mechanism is the prime hallmark of the designed optimizer in avoiding local power peaks and excessive global search operations. The developed algorithm was examined through diverse complicated partial shading conditions to challenge its global and local search abilities. A comparative analysis was carried out against the well-regarded PSO, GWO, and the standard slime mould algorithm. In overall, the designed optimizer defeated its contenders in all aspects offering higher efficiency, superior robustness, faster convergence, and fewer fluctuations to the operating point. An experimental setup that consists of the DSpace microcontroller and a PV emulator was employed to validate the algorithm overall performance. The recorded outcomes outline that the developed optimizer can achieve a tracking time of 0.6 seconds and 0.86 seconds on average, with 99.85% average efficiency under complex partial shading conditions.

Visual Re-Initialization Model Development Methodology for Solving Problems Regarding Metaheuristic Algorithm-Based MPPT Applications

Metaheuristic algorithms are particularly useful for maximum power point tracking (MPPT) applications, because they can adapt to changes in operating conditions and effectively handle partial shading conditions. However, metaheuristic algorithms also have some limitations that need to be addressed to make them suitable for MPPT applications. The problems associated with metaheuristic algorithm-based MPPT applications include being trapped in local optima, slow convergence speed, shading condition variability, computational complexity and robustness. These problems lead to reduced efficiency in MPPT applications. In the literature, the solution of the aforementioned problems is partially addressed and some of them are solved via an additional irradiation sensor. The motivation of this study is to develop a control algorithm that covers all problems that have been partially solved in the literature and includes an original re-initialization modeling method in accordance with visual programing concept, without using any additional radiation sensor. The proposed control algorithm has the flexibility to be easily adapted to other metaheuristic algorithms and does not require any radiation sensors. The re-initialization model created via Matlab/Simulink and “Embedded Coder Support Package for TI C2000 Processors” allows easy tracking of the global maximum power point (GMPP) by detecting variable radiation conditions. The proposed model was implemented on the Cuckoo Search Algorithm (CSA) and verified through experimental studies carried out with a TI-TMS320f28069 microcontroller and PV emulator. The experimental results confirm that issue 1 is solved with 100%, issue 2 is solved with 99.5%, issue 3 is solved with 99.84%, and issue 4 is solved with 100% MPPT efficiency.

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.

LOW-COST PHOTOVOLTAIC EMULATOR WITH A HIGH-DYNAMIC RESPONSE FOR SMALL SATELLITE APPLICATIONS

In recent years, growing interest from academic institutions and emerging countries in small satellite missions has led to a need for photovoltaic emulators (PVEs) during subsystem testing. Unfortunately, the market lacks suitable PVEs with adequate power and characteristics for these applications. To fill this gap, this proposal introduces a low-cost, flexible, and high-dynamic response PVE for testing small satellite subsystems. The proposed PVE includes an analog electronic circuit that amplifies the reference current-voltage (I-V) curve from a space reference solar cell. It is characterized by accurate amplification of solar cell curves under various conditions, achieving a maximum output power of 124 W. This capability enables assessment under diverse space application scenarios with rapidly changing climatic conditions. The emulator's remarkable flexibility is demonstrated through the use of various solar cell technologies for space applications as references. Experimental results effectively confirm the practical feasibility of the proposed PVE.

Performance Analysis of Bus Voltage in Distribution Network with High Penetration of PV Controlled via Data-Driven Approach

Integration of large-scale distributed photovoltaic (PV) generation resources can lead to technical challenges, particularly voltage rise caused by PVs power injection at the time of high solar radiation profile and low load demand. Reactive power control of PV inverters can help mitigate the voltage rise, which arises just for a short duration due to high incident solar radiation. There is a possibility to control functions on these PV inverters based on the rating of the PV inverter. For one-second resolution, local data-driven voltage sensitivity estimation is applied to update the control functions such as volt-var and volt-watt on the PV inverters so as to regulate the bus voltage. Different forms of solar radiation profiles, transient varying, smooth varying, and worst operating scenario corresponding to the minimum load at the time of high PV generation, are included. The study is demonstrated for different PV penetration levels. The voltage analysis on buses is performed using power hardware (PV emulator) in the loop simulation, at one of the buses in the network, while the remaining PVs are being controlled using volt-var control (VVC)/volt-watt control (VWC) methods. The applied approach confirms to maintaining bus voltage within limits even against a very short transient time instant of solar radiation profile.

A New Piecewise Segmentation Based Solar Photovoltaic Emulator Using Artificial Neural Networks and a Nonlinear Backstepping Controller

A New Piecewise Segmentation Based Solar Photovoltaic Emulator Using Artificial Neural Networks and a Nonlinear Backstepping Controller

A New High-Performance Photovoltaic Emulator Suitable for Simulating and Validating Maximum Power Point Tracking Controllers

Photovoltaic (PV) research is rapidly growing, and the need for controlled environments to validate new MPPT controllers is becoming increasingly important. Currently, researchers face several challenges in testing MPPT algorithms due to the unpredictable nature of solar PV power generation. In this paper, we propose a new photovoltaic emulator (PVE) that could replace solar panels and ensure a highly controllable environment suitable for testing photovoltaic (PV) systems. In this PVE, the complex nonlinear equations of the PV cell/module are fast computed and resolved by a new linearization technique which involves the systematic breakdown of the current-voltage ( I - V ) curve of the PV into twelve linear segments. Based on input environmental conditions, an artificial neural network (ANN) was constructed to assist the linearization process by predicting the current-voltage boundary coordinates of these segments. Using simple linear equations, with the segment boundary coordinates, a reference voltage was generated for the PVE. A nonlinear backstepping controller was designed to exploit the reference voltage and stabilize the power conversion stage (PCS). The PVE was optimized using particle swarm optimization (PSO). Several tests have shown that the proposed nonlinear controller provides better dynamic and robust performance than the PI controller, the most reputable and recurrent control method in the area of PVE. The PVE was coupled with a recently proposed integral backstepping MPPT controller and analyzed under several dynamic conditions, including the MPPT test specified by EN 50530. It was found that the accuracy of the proposed PVE measured by its relative error is less than 0.5%, with an MPPT efficiency of greater than 99.5%. The attractive results achieved by this PVE make it especially suitable for simulating and validating MPPT controllers.

A Design and Validation of 400 W PV Emulator Using Simple Equivalent Circuit for PV Power System Test

In this paper, we propose a photovoltaic emulator (PVE) composed of a series of connected power diodes with a single constant current source, rather than a power converter-based PVE. Accordingly, this enables readily verifying the operation because a the simple hardware structure and lack of a complex control algorithm. The proposed PVE can be intuitively implemented using the proposed selection criteria for the power diode and equivalent resistances. Since there is no feedback control based on complex analog/digital controls and sensors, the control response can be very fast. In addition, the proposed PVE can be easily integrated with a PV power system such as a power optimizer, to allow testing in a simple and flexible manner. Spice simulation was performed based on the electrical characteristics provided by the solar panel manufacturer, and this was utilized to validate the applied emulator circuit model, the Norton Equivalent Circuit. During the design process, the simulation helped to manufacture the PVE within error ranges satisfying the desired I–V and P–V curves, as well as the maximum power point (MPP). Partial shading could be easily implemented through use of the multiple series connection of individual PVEs, demonstrating local MPP and global MPP. A 400 W class PVE was built through appropriate power diode selection, with a thermal design to increase the output power. Moreover, its performance and feasibility were verified through intensive experiments. The measured efficiency, transient response time, and maximum transient error of the partial shading tests were 91%, 22 μs, and 5.8%, respectively.

Modeling and Control of PV Emulator with Different Controllers and Transient Load Conditions

To keep up with the pace of renewable energy, PV Emulators are encouraged during the design and installation stages. Short circuit current, maximum power point and open circuit voltage are required to analyze the complete characteristic plot of PV panel.This paper focuses on the modeling and control of PV Emulators, as well as the comparison of the results obtained by implementing P,PI, PID and FOPID as conventional controllers with AI-based PSOPI, PSOPID and ANFIS controllers. This work will aid in minimizing time, cost and on-site constraints, allowing timely installation of PV panels after covid. Another distinguishing feature of this paper is the comparative analysis of designed models with various control strategies and their associated performance indices over complete range of PV characteristics.

Low cost microcontroller implementation of Takagi–Sugeno Fuzzy MPPT controller for photovoltaic systems

Maximum power point trackers have a significant role in optimizing the energy conversion efficiency in a photovoltaic system. The numeric achievements of MPPT algorithm can be implemented and tested by using several embedded boards as Digital Signal Processor, Field-Programmable Gate Array, Arduino, and dspace. Alternatively, for the low cost, availability and simplicity, the PIC microcontrollers can be used compared with the other hardware devices. Therefore, this paper presents the implementation of a Takagi–Sugeno fuzzy controller on a low cost PIC microcontroller, for tracking the maximum power point of a PV module. The PV system consists of a PV emulator, DC-DC converter, and resistive load. The different steps in design, simulation and realization of the T-S Fuzzy logic controller are discussed. The proposed controller system was evaluated by comparing its performance metrics, in terms of efficiency and the Integral Square Error, with existing technique in the literature. The results corresponding to the experimental validation show that the proposed MPPT controller is able to ensure a perfect tracking of the maximum power point with variation of irradiance and is performing better than reported in a previous work.

Accurate, Fast and Power Efficient PV Emulator Based on Hybrid Passive and Active Circuits

A photovoltaic emulator (PVE) has been proposed based on a physical equivalent photovoltaic cell model. It has fast dynamic performance that is compatible with a real PV system. The PV emulator can analyze and assess PV systems, including maximum power point tracking (MPPT). It is power efficient at the maximum power point. However, it has higher power loss at an open-circuit voltage (OCV). This paper, therefore, presents a hybrid solution using a switching circuit (SC) paralleled with the diode string (DS) to minimize the power loss. The SC consists of a two-switch non-inverting buck-boost dc/dc converter. The DS operates with a low power loss region, i.e., the current source region (CSR) of the I-V curve, in which the SC switches in to replace the DS in the voltage source region (VSR) to minimize power loss of the DS while maintaining the emulator’s circuit operation. The SC handles only a fraction of the rated emulator power and has a much narrower control bandwidth than a pure switching converter-based solution. Experimental results show that in the worst-case scenario, i.e., OCV condition, the efficiency and temperature of the PVE based on DS alone configuration reach 2.8% and 94.2 °C, respectively, as compared to 85.98% and 26.5 °C for the proposed hybrid solution. Under the 30% to 60% insolation change test, the suggested PV emulator lags the actual PV panel by only 3.2 ms, compared to 120 ms lagging for a commercial emulator. Furthermore, the control strategy is implemented to handle the trade-off between thermal and dynamic performances of the proposed solution.

Maximum Power Point Tracking Implementation under Partial Shading Conditions Using Low-Cost Photovoltaic Emulator

Maximum power point tracking (MPPT) is a pivotal objective for photovoltaic (PV) systems. To test various MPPT techniques, a reliable and effective PV emulator is required. Therefore, this article proposes a low-cost PV emulator for partial shading conditions, in which a cascaded structure of a DC power source with a resistor is constructed to generate the multiple peaks of the power-voltage (P-V) curve. The proposed structure is simple and modular. Consequently, it can be extended to obtain several peaks in the P-V characteristics to emulate more complex partial shading conditions. The partial shading occurrence over the PV source (PV array) causes a significant power loss production from the PV system. To increase the PV system’s efficiency, optimization techniques are employed to harness the global power. Accordingly, the particle swarm optimization (PSO) technique is used to track the global peak. Furthermore, the conventional perturb and observe (P&O) method is applied for comparison and investigation. The proposed PV emulation system is validated under different operating conditions using simulation and experimental hardware-in-the-loop (HIL) results.

Design and Implementation of an Improved Metaheuristic Algorithm for Maximum Power Point Tracking Algorithm Based on a PV Emulator and a Double-Stage Grid-Connected System

Design and Implementation of an Improved Metaheuristic Algorithm for Maximum Power Point Tracking Algorithm Based on a PV Emulator and a Double-Stage Grid-Connected System

Photovoltaic emulator using error adjustment fuzzy logic proportional-integral controller

The photovoltaic (PV) technology has been increasingly used in our energy generation. Therefore, it is essential to have a good PV testing facility during the development process. The PV emulator (PVE) is a voltage or current source that mimic the current-voltage characteristic as a PV module that requires proper control strategy to work. The resistance feedback method (RFM) control strategy has many good attributes, except the transient response, which is caused by the proportional-integral (PI) controller. This paper proposed a new fuzzy logic PI (FLPI) controller to improve the transient performance of the RFM PVE. It is based on the error adjustment method that founded on the transient state and load of the PVE. The performance of the proposed PVE is compared with the original PVE that used RFM with the PI controller. The finding of the research shows that the transient performance of the proposed PVE has improved 2.3 times compared to the original PVE without affecting its accuracy.