Performance Of Photovoltaic System Research Articles

Performance enhancement of photovoltaic system using composite phase change materials

Solar photovoltaic (PV) systems are becoming a more feasible energy source. Energy storage devices can increase Photovoltaic (PV) system performance when PV module temperature rises and electrical efficiency declines. This work uses Lauric and Palmitic acid composite phase change material to evaluate polycrystalline solar panels in real-time. The best Lauric and Palmitic acid ratio was 3:1 in experiments. Three other solar panels were fabricated using 7, 10, and 14 composite-filled Al pipes at the bottom, apart from the reference panel. Compared to panels with 7 and 10 Al-pipes, the L + P CPCM 14 Al-pipes panel had the greatest mean electrical output of 38.56 W, a peak average efficiency of 15.23 % while the average temperature was 43.74 °C. The highest temperature reduction of the CPCM was 26.2 % and the energy stored was 28.78 kJ. The CPCM exhibited a maximum power improvement of 39.9 % and an efficiency improvement of 31.1 %. However, the reference panel had the maximum mean electrical output of 27.56W and peak mean efficiency of 12.78 % only if all days of experimentation were considered.

A Fast Reconfiguration Technique for Boost-Based DMPPT PV Systems Based on Deterministic Clustering Analysis

Mismatching operating conditions affect the energetic performance of PhotoVoltaic (PV) systems because they decrease their efficiency and reliability. The two different approaches used to overcome this problem are Distributed Maximum Power Point Tracking (DMPPT) architecture and reconfigurable PV array architecture. These techniques can be considered not only as alternatives but can be combined to reach better performance. To this aim, the present paper presents a new algorithm, based on the joint action of the DMPPT and reconfiguration approaches, applied to a reconfigurable Series-Parallel-Series architecture, which is suitable for domestic PV application. The core of the algorithm is a deterministic cluster analysis based on the shape of the current vs. voltage characteristic of a single PV module combined with its DC/DC converter to perform the DMPPT function. Experimental results are provided to validate the effectiveness of the proposed algorithm and to demonstrate evidence of its major advantages: robustness, simplicity of implementation and time-saving.

Solar insolation and PV module temperature impact on the actual grid connected solar photovoltaic system in German Malaysian Institute

German Malaysian Institute (GMi) has a great potential for utilising solar photovoltaic (PV) electricity. In this study, the performance of a 3kW grid-connected solar PV (GCSPV) system is monitored using Sunny Portal web in conjunction with Sunny SensorBox. The daily solar energy that strikes a PV array cannot be totally transformed into grid-compatible electricity. As a result of power losses, the production of solar PV systems falls short of expectations. The primary objective of this study is to examine the influence of solar insolation and PV module temperature on the actual output of the GCSPV system compared to the predicted output system by considering elements that affect the conversion of solar power to electrical power in GCSPV systems. The study prediction method based on calculations was carried out. Prediction of output system considered all losses, including efficiency of PV module, solar insolation, temperature of PV module, dirt, manufacturer’s tolerance and module mismatch, the voltage drop in DC cables to inverter, and inverter efficiency. The data indicate that the average power generation is 7.26% lower than predicted by the system. The GCSPV system’s performance efficiency is diminished by the temperature increase and somewhat enhanced by an increase in solar insolation. Temperature has a crucial impact in the energy conversion process of PV module. The effectiveness of the PV module in generating energy and power is dependent on its operating temperature.

An Enhanced Filtering Generalized Integrator-Based Control for Improved Performance of a Grid-Tied PV System at Adverse Grid Voltages

This paper deals with a single stage photovoltaic (PV) system with improved resilience against adverse grid conditions. This is majorly achieved in two ways. First, it presents an enhanced filtering generalized integrator (EFGI) based filtering stage, to process the prevalent grid voltages and derive their positive sequence components free of harmonics components, unbalance, and DC offset. The presented EFGI delivers a considerable improvement over the conventional filters and helps to improve the power quality (PQ) in case of unequal or distorted voltage conditions. Second, to address large voltage dip or rise scenarios, a dual mode mechanism is applied for the selection of the DC link voltage ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> ) reference and the corresponding generated PV array power. At regular voltage scenario and at availability of PV power, the reference <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> is governed by the necessity to keep the PV array operating at its peak generation levels. However, at loss of PV power or divergent grid voltages, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> control is quickly transferred to an alternate approach, varying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> as per the existing grid voltages. Using this, the injected currents are limited, and system de-synchronization is prevented at voltage dips. The increased <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> at grid voltage rise avoids deterioration in the grid PQ. The grid PQ is also assured despite the presence of local non-ideal loads. The EFGI benefits and the real time performance of the presented system, are assessed using test results.

Experimental investigation on photovoltaic thermal system performance with different attachment techniques

Photovoltaic thermal (PVT) systems promise a more optimal solar energy utilization by producing electricity and heat simultaneously. However, the thermal collector design and the way it is connected to the PV panels are still a problem for the PVT system. This study aims to answer the use of thermal paste and insulation in thermal collectors are some of the issues in PVT system manufacturing. An experimental study was conducted by making three scenarios of thermal collectors combined with PV panels and then tested in an indoor solar simulator. Each design was tested with five different cooling water flow rates. This study found that thermal paste and insulation can improve the electrical power of PVT systems by up to 17.59% and its thermal performance increase by 10.13%. Thus, the use of thermal paste and insulation should be done in the manufacturing of each thermal collector for PVT systems.

Tuning proportional integral controller to enhance the photovoltaic system performance

This paper presented a closed loop buck-boost converter controlled by using proportional and integral controller (PIC) for photovoltaic (PV) applications. The mathematical equations of design the solar panel type KC200GT and buck–boost converter is illustrated. The electrical behaviors of solar panel are examined at 1,000 light radiation and room temperature (25 °C) situation. A PIC tuning is difficult with conventional methods such as graphs and mathematical analysis, so for that reason two approaches are used for tuning and optimization of PIC, particle swarm optimization (PSO), and Zeigler–Nichols (ZN). The suggested closed loop application through the converter preserves the constant voltage in the output spite of alterations to the input voltage, decreases settling time, overshoot and ripple voltage to minimum value that enhancement the efficiency of the converter. The results show that the PSO approach is improved than the ZN approach. The proposed model is constructed by using SimPower system tool box in MATLAB package.

Enhanced performance of photovoltaic thermal module and solar thermal flat plate collector connected in series through integration with phase change materials: A comparative study

The decline in photovoltaic cell efficiency due to temperature elevation, coupled with solar energy's inherent intermittency, presents challenges for solar systems, including photovoltaic panels and solar thermal (ST) collectors. The integration of phase change materials (PCMs) emerges as a promising solution to enhance thermal energy storage and regulation, thereby improving system performance and sustainability. This study investigates the performance of series-connected photovoltaic thermal (PVT) and ST systems with integrated PCMs from energy and exergy viewpoints, utilizing a 3D transient numerical simulation. The proposed system's performance is compared with that lacking a PCM across various heat transfer fluid mass flow rates. Suitable PCM selection based on melting temperature is explored, along with PCM layer thickness and nanoparticle augmentation. The research highlights that PCM integration effectively regulates PV cell temperature, enhancing electrical power and exergy rate. While increasing the mass flow rate improves thermal and electrical power, it reduces the thermal exergy rate. Comparing different PCMs (RT28 HC, RT35 HC, and RT44 HC), the RT28 HC PCM provides the lowest temperature of PV cells and the best electrical performance during the day. Moreover, increasing PCM layer thickness results in lowering PV cell and outlet temperatures and storing more heat. Nanoparticles added to PCMs lead to a marginal improvement in energy and exergy, especially for the system with RT28 HC. This analysis emphasizes PCM integration's role in enhancing solar thermal system performance, highlighting the importance of considering the PCM's melting temperature for effective thermal energy management.

PLANNING AND ANALYSIS OF HYBRID SOLAR POWER PLANT AT PUBLIC ELECTRIC VEHICLE CHARGING STATIONS AT NUSA PUTRA SUKABUMI UNIVERSITY

The increasing use of fossil fuel vehicles causes an increase in air pollution. Electric vehicles are an environmentally friendly transportation alternative, but the problem of charging electric motorbike batteries is a limiting factor to their use. Especially in areas where battery charging facilities are still limited. This study aims to simulate the performance of a hybrid photovoltaic (PV) system using HOMER software for charging batteries on electric motorbikes at the University of Nusa Putra Sukabumi. This research conducts a feasibility study, calculates the Net present Cost (NPC) Cost of Energy (COE) and Break Event Point (BEP) energy requirements to design a PV system, and simulates its performance using HOMER software. This research produces a renewable value fraction of 25.3%. The design of the Hybrid system has been achieved because all the components of the hybrid system at SPBKLU of Nusa Putra University have been fulfilled consisting of 22 545 Wp solar panels, 2 48 V 50 Ah batteries, and 4 4 kW Hybrid Inverters. Based on the economic analysis, the NPC value is IDR 165,882,507, the initial capital cost is IDR 61,432,496, the Cost of Energy is IDR 120,421 /kWh, and the BEP for 11 years

Rapid parameter identification of three diode photovoltaic systems using the Cheetah optimizer

This study focuses on accurate parameter identification for solar cells and photovoltaic module simulation using experimental data. To tackle the challenge of modelling these highly nonlinear systems, we propose the novel use of the Cheetah Optimizer (CO) algorithm, inspired by cheetah hunting strategies. The CO algorithm employs mathematical models and randomization parameters to balance exploration and exploitation, avoiding local optima by considering energy limitations. We demonstrate the CO algorithm's effectiveness by applying it to the three-diode model in solar photovoltaic systems, specifically the STP6-120/36 and Photowatt-PWP201 PV modules. Impressively, the CO algorithm achieves remarkably low root mean square error values of 0.0145 A and 0.0019 A, outperforming state-of-the-art methods and ensuring high accuracy. Additionally, it delivers the lowest power errors of 0.16054 W and 0.01484 W for the respective modules, highlighting its exceptional performance. The CO algorithm proves to be a promising tool for precise parameter extraction and optimization, leading to improved modelling and performance of solar photovoltaic systems.

Artificial intelligent control of energy management PV system

Renewable energy systems, such as photovoltaic (PV) systems, have become increasingly significant in response to the pressing concerns of climate change and the imperative to mitigate carbon emissions. When static converters are used in solar power systems, they change the current, which uses reactive energy. A proportional-integral controller regulates active and reactive powers, whereas energy storage batteries enhance energy quality by storing current and voltage as they directly affect steady-state error. The utilization of artificial intelligence (AI) is crucial for improving the energy generation of PV systems under various climatic circumstances, as conventional controllers do not effectively optimize the energy output of solar systems. Nevertheless, the performance of PV systems can be influenced by fluctuations in meteorological conditions. This study presents a novel approach for integrating solar PV systems with high input performance through adaptive neuro-fuzzy inference systems (ANFIS). A fuzzy neural inference-based controller regarding energy generation and consumption aspects was designed and examined. This study examines the importance of artificial intelligence in facilitating continuous power supply to clients using a battery system, hence emphasizing its significance in energy management. Moreover, the findings demonstrated promising outcomes in energy regulation and management.

On the use of reference modules in characterizing the performance of bifacial modules for rooftop canopy applications

The growth of bifacial systems in different photovoltaic (PV) applications is driving new requirements in measuring the effective irradiance for bifacial PV. Non-uniformity of rear irradiance may have a significant impact on the proper characterization of the system. It also has implications on modeling rear irradiance and consequently on performance prediction. In this work, we present a simple way of constructing an irradiance sensor from a commercial bifacial module and the application to performance and rear-side irradiance variability of a small rooftop bifacial PV system is analyzed. In addition, assessment of some commonly used models based on the view-factor approach is performed using as reference the front and rear irradiance simultaneous measurements from the bifacial reference module. The use of bifacial reference modules implies some benefits over arranging calibrated cells for determining rear irradiance, due to their higher field-of-view and representativeness of the response of the system to be monitored. This work also illustrates the high variability of rear irradiance in small systems and the impact of the edge effect. The results here can contribute to a better knowledge of the use of these reference modules as effective irradiance sensors for very bifacial PV configurations.

Design of an intelligent controller for improving the solar system efficiency

Ensuring the optimal performance of photovoltaic systems necessitates the development of a maximum power point tracker MPPT aimed at extracting the utmost power from the photovoltaic array. This study delves into the efficacy of a fuzzy logic controller compared to conventional controllers designed for tracking the maximum power point. The simulation model, employed for this research work, is implemented using Matlab/Simulink. a detailed comparison between the classic Perturb and Observe P.O algorithm and an other intelligent based on the fuzzy algorithm is conducted to assess MPPT accuracy. The findings demonstrate that the proposed model is characterized by simplicity and the capability to simulate diverse operating conditions.

A critical review of PV systems’ faults with the relevant detection methods

PhotoVoltaic (PV) systems are often subjected to operational faults which negatively affect their performance. Corresponding to different types and natures, such faults prevent the PV systems from achieving their nominal power output and attaining the required level of energy production. Regarding the operational optimization of PV systems, this paper aims primarily at surveying and categorizing different types of PV faults, classified as electrical, internal, and external, where each is thoroughly investigated: internal faults occur at the PV cellular level, and can either be short circuit, open circuit, bridging, or bypass diode faults. External faults on the other side are mainly classified as temporary (i.e., clouds shading, snowstorms, etc.) or permanent (e.g., glass breakage, frame defects, etc.) mismatch faults. Lastly, electrical faults involve common circuitry problems, such as short circuits (e.g., line to ground, line to line, etc.), power processing units’ faults (e.g., inverter faults), and arc faults. As for the detection methods, six major fault detection methods are investigated for the AC side of the PV system with twenty-nine total AC based fault detection methods. On the other hand, eleven major fault detection methods are surveyed for the DC side of PV systems with seventy-three total DC based fault detection methods. The investigated methods are critically analyzed, and compared relevantly to each other, within the mutual sub-sets. The resulting tabulated comparative data assessments for PV faults (i.e., cause-effect relationships, impact on the PV system performance), as well as for faults detection methods (i.e., priority for application, etc.) compose a rich background for related PV systems’ performance security fields, where a nexus future work is also suggested.

A New MPPT-Based Extended Grey Wolf Optimizer for Stand-Alone PV System: A Performance Evaluation versus Four Smart MPPT Techniques in Diverse Scenarios

Photovoltaic (PV) systems play a crucial role in clean energy systems. Effective maximum power point tracking (MPPT) techniques are essential to optimize their performance. However, conventional MPPT methods exhibit limitations and challenges in real-world scenarios characterized by rapidly changing environmental factors and various operating conditions. To address these challenges, this paper presents a performance evaluation of a novel extended grey wolf optimizer (EGWO). The EGWO has been meticulously designed in order to improve the efficiency of PV systems by rapidly tracking and maintaining the maximum power point (MPP). In this study, a comparison is made between the EGWO and other prominent MPPT techniques, including the grey wolf optimizer (GWO), equilibrium optimization algorithm (EOA), particle swarm optimization (PSO) and sin cos algorithm (SCA) techniques. To evaluate these MPPT methods, a model of a PV module integrated with a DC/DC boost converter is employed, and simulations are conducted using Simulink-MATLAB software under standard test conditions (STC) and various environmental conditions. In particular, the results demonstrate that the novel EGWO outperforms the GWO, EOA, PSO and SCA techniques and shows fast tracking speed, superior dynamic response, high robustness and minimal power fluctuations across both STC and variable conditions. Thus, a power fluctuation of 0.09 W could be achieved by using the proposed EGWO technique. Finally, according to these results, the proposed approach can offer an improvement in energy consumption. These findings underscore the potential benefits of employing the novel MPPT EGWO to enhance the efficiency and performance of MPPT in PV systems. Further exploration of this intelligent technique could lead to significant advancements in optimizing PV system performance, making it a promising option for real-world applications.

IoT-based wireless data acquisition and control system for photovoltaic module performance analysis

The development of photovoltaic (PV) technology has led to an increasing demand for efficient and reliable monitoring systems that can ensure the optimal performance of PV modules. In particular, remote monitoring systems are critical for PV systems installed in distant locations where manual monitoring is difficult and expensive. In this article, we introduce a low-cost wireless monitoring system that employs NodeMCU boards, Raspberry Pi, and Internet of Things (IoT) technologies to monitor and analyze the operational and environmental data of PV modules. By using free and open-source software packages including MQTT Mosquitto Broker, InfluxDB, Node-RED, and Grafana, the system can collect, store, and visualize data, offering an effective means of studying a PV system performance from anywhere. The proposed system is found to work efficiently under different testing conditions; providing precise measurements for different parameters, including voltage, current, power, ambient temperature, humidity, panel temperature, PV coolant temperature at inlet and outlet, and spectral irradiance. In addition to the data collected by the set of sensors, the system provides another source of environmental data using a web-based service called "OpenWeatherMap" that provides a complete global forecast. Alerts and thresholds have been successfully implemented in our system, providing an added advantage of being able to detect and respond to any anomalies in the collected data. Furthermore, the system provides two operational controls: wireless ON/OFF control and wireless control of a servo motor which can be utilized in various applications improving overall system performance.

Experimental energy performance assessment of a bifacial photovoltaic system and effect of cool roof coating

In the quest for high albedo materials that boost the energy production of bifacial photovoltaic systems, a range of material already exists for reducing building roof surface temperatures, called cool roof materials. However, there is a noticeable absence of scientific literature addressing the combination of cool roofs and bifacial photovoltaic systems. This study investigates the photovoltaic performance of a bifacial photovoltaic system with cool roof coating on the underside and its impact on floor temperature. For this purpose, four ∼1kWp prototypes were installed on the terrace of the GAIA building of the UPC near Barcelona, Spain: (1) bifacial panels above a cool roof, (2) bifacial panels above normal floor, (3) bifacial panels above a normal floor with n-type solar cells encapsulated in TPO, and (4) monofacial panels. The results reveal 8.6 % higher PV yield for bifacial with cool roof compared to monofacial, and 4–4.5 % higher for bifacial (normal floor) compared to monofacial. Additionally, the cool roof coating contributes to reducing the floor temperatures, particularly in the unshaded (exposed) areas during summer (−3.8 °C). The presence of photovoltaic panels has also demonstrated a positive impact on floor temperatures during both winter and summer. Thus, the cool roof coating offers two benefits: increased photovoltaic yield and reduced building cooling requirements, both of which are associated with economic advantages. The cool roof coating can be integrated into existing or new bifacial roof systems.

Integrated Control and Optimization for Grid-Connected Photovoltaic Systems: A Model-Predictive and PSO Approach

To propel us toward a greener and more resilient future, it is imperative that we adopt renewable sources and implement innovative sustainable solutions in response to the escalating energy crisis. Thus, renewable energies have emerged as a viable solution to the global energy crisis, with photovoltaic energy being one of the prominent sources in this regard. This paper represents a significant step in the desired direction by focusing on detailed, comprehensive dynamic modeling and efficient control of photovoltaic (PV) systems as grid-connected energy sources. The ultimate goal is to enhance system reliability and ensure high power quality. The behavior of the suggested photovoltaic system is tested under varying sun radiation conditions. The PV system is complemented by a boost converter and a three-phase pulse width modulation (PWM) inverter, with MATLAB software employed for system investigation. This research paper enhances photovoltaic (PV) system performance through the integration of model-predictive control (MPC) with a high-gain DC–DC converter. It improves maximum power point tracking (MPPT) efficiency in response to the variability of solar energy by combining MPC with the traditional incremental conductance (IN-C) method. Additionally, the system incorporates a DC–AC converter for three-phase pulse width modulation, which is also controlled by predictive control technology supported by Particle Swarm Optimization (PSO) to further enhance performance. PSO was selected due to its capability to optimize complex systems and its proficiency in handling nonlinear functions and multiple variables, making it an ideal choice for improving MPC control performance. The simulation results demonstrate the system’s ability to maintain stable energy production despite variations in solar irradiation levels, thus highlighting its effectiveness.

Analisis Beberapa Jenis PLTS di Khatulistiwa Menggunakan Prototype alat ukur PV

Photovoltaic is employed to support the national electricity energy blending policy as an additional component of renewable energy. Efficient energy monitoring is utilized to understand the efficiency, energy requirements, and performance of Photovoltaic. This research aims to analyze the performance of a standalone Photovoltaic system. The study considers various parameters such as PV size, load, and charge controller. Additionally, it involves designing a monitoring tool used for the analysis of Photovoltaic performance. Performance measurements are conducted by comparing Photovoltaic systems of different sizes. Furthermore, performance measurements are compared with different types of charge controllers. To assess performance, solar energy is measured at various times using a solar power meter. The solar power meter is then used to analyze the research's performance. Energy monitoring involves measuring voltage, current, power, and energy using Atmega328p. The measurement data is utilized to evaluate system efficiency and design future systems. Monitoring is carried out directly on Photovoltaic systems in the equatorial region. This research measures solar energy, energy produced by Photovoltaic systems, and energy consumed by loads. Energy monitoring focuses on observing solar and Photovoltaic energy during sunlight hours (morning-evening). The analysis results can be utilized for designing efficient Photovoltaic systems in the future..

Hybrid SSA-PSO based intelligent direct sliding-mode control for extracting maximum photovoltaic output power and regulating the DC-bus voltage

Photovoltaic (PV) energy has become a considerably more attractive source of power, with costs continually declining. Along with the implementation of this sustainable energy system, the power supply stability should be extensively investigated, particularly in off-grid systems where electricity storage becomes a limitation. The performance of the standalone photovoltaic battery system (SPVBS) is mainly reliant on the DC-bus voltage and its capacitor. The shortcomings of utilizing a small film DC-bus capacitor in SPVBS include the instability and low-frequency ripple of DC-bus voltage and the system's dynamic MPPT techniques performance. Therefore, this study proposes a new technique of regulating the various system components that stabilizes the DC-bus voltage, extracts stable output power despite harsh sudden load and radiation fluctuations, and maintains the system's functionality with or without the battery storage system (BSS). The proposed approach uses a hybrid salp swarm algorithm and particle swarm optimization (SSA-PSO) combined with an intelligent direct sliding mode controller (DSMC) to eliminate the instability and ripples of DC-bus voltages. Moreover, it employs DSMC for bidirectional DC-DC converter and boost converters to enhance the system's dynamic MPPT techniques capabilities. Four different modes are considered according to the energy availability, demand, and battery's state of charge (SOC). Additionally, this easy-to-use technique keeps the DC-bus voltage stable even when the battery storage system is not available. Simulation and experimental results revealed the resilience performance of the proposed intelligent SSA-PSO-DSMC technique in terms of DC-bus regulation, output power extracted, and current harmonics reduction compared to PSO, SSA, cuckoo search optimization (CSO), and grey wolf optimization (GWO).

Assessing the combined effects of local climate and mounting configuration on the electrical and thermal performance of photovoltaic systems. Application to the greater Sydney area

Extremely high urban temperatures adversely affect photovoltaic (PV) system performance. Accurate PV cell temperature assessment relies on local weather conditions, exacerbated by urban overheating, often overlooked by inadequate temperature models and non-local data. This study investigates the electrical and thermal PV performance, considering mounting configurations and local conditions. Data from ten weather stations in Greater Sydney (NSW) during 2016–2017, including a hot summer, are used. The Sandia model is used to predict cell temperatures and power output for four mounting configurations, from open rack to building-integrated (BIPV). A PV thermal model is implemented to analyse daytime convection, crucial for understanding PV impact on local climate. Results show peak cell temperatures of 60 °C (open rack) to over 90 °C (BIPV), causing up to 50% power loss and 11% reduction in monthly performance ratio. Local climate variations impact PV energy output up to 6%, with mounting configuration effects up to 11%. Daytime convective flux averages 150–180 W/m2, peaking at 700 W/m2. Convective release varies up to 22% based on local climate, generally higher for open rack than close roof mounts, with potential reversals under low wind speed conditions. These findings can improve the knowledge of PV performances in urban areas facing extreme temperatures.