Efficiency Of Solar Power Systems Research Articles

A Novel Strategy for Multitype Fault Diagnosis in Photovoltaic Systems Using Multiple Regression Analysis and Support Vector Machines

This study focuses on analyzing common fault types in photovoltaic (PV) modules, employing fault diagnosis methods based on machine learning technology to enhance the accuracy and efficiency of diagnosing faults in solar power systems. Initially, we collected relevant data from the solar power system and used data analysis techniques to identify system faults, designing a human-machine monitoring interface for practical application. Furthermore, the experimental results proved that the system could accurately identify eight major types of faults, including solar panel output circuits, energy storage batteries, maximum power point tracking (MPPT) controllers, inverters, dust accumulation, loosening of mounting rack screws, damage to the mounting rack foundation, and deformation of the mounting rack structure. Particularly in the detection of dust accumulation, we developed a new method of estimating power generation from multiple regression analysis (MRA), which closely aligns the estimated power output with the actual power output, highlighting the significant impact of dust accumulation on the efficiency of solar power systems. Next, by integrating voltmeters and support vector machines (SVM) into the solar PV array modules, we are able to quickly and accurately measure and locate short-circuit and open-circuit faults in bypass diodes. Ultimately, the proposed PV fault diagnosis strategy includes diagnostics for dust accumulation and mounting frame faults, making it particularly suitable for areas with severe air pollution and frequent earthquakes, providing a comprehensive fault diagnosis solution.

Carbon nanotubes reinforced poly (methyl methacrylate) polymer composites for the application as classic performance quasi-solid-state dye-Sensitized solar cell electrolytes

Our study aimed to evaluate the efficiency of solar power systems by developing dye-sensitized solar cells (DSSCs) with an electrolyte of carbon nanotubes (CNTs) dispersed in poly (methyl methacrylate) (PMMA). Photovoltaic and impedance spectral studies showed that a polymer electrolyte with 3% carbon nanotubes resulted in an efficiency of solar power conversion of 7.40% and a photovoltaic current per unit cross-sectional area of 17.13 mA/cm2 in DSSCs. The optimal distribution of CNT fillers decreased the charge recombination of the titanium dioxide-dye-CNT-PMMA electrolyte surface. The X-ray diffraction spectrum showed characteristic peaks of PMMA and CNT at 28.6° and 63.0°, respectively, obtained in pure form and composites with dispersed carbon nanotubes. The peak intensity increased with the addition of CNTs in the polymer. A Fourier transform infrared spectroscopy investigation revealed the bonding within the CO and CH of PMMA and CNT to the polymer composite. The dielectric constant increased from 2.9 to 4.4, and the dielectric loss increased from 0.10 to 0.90 at 1 MHz for the dispersion of 6% CNT in PMMA. Scanning electron microscopy analysis showed that the internal particle structure and porosity had changed due to the presence of the CNTs in the PMMA. The dispersion of CNTs into the PMMA increased the maximum voltage of the polymer electrolytes. A breakdown voltage of 7.4 kV was attained with 6% CNTs in the polymer.

EPCMSDB: Design of an ensemble predictive control model for solar PV MPPT deployments via dual bioinspired optimizations

With the increasing demand for renewable energy, solar power has emerged as a promising option for sustainable power generation. However, the effectiveness and efficiency of solar power systems rely on the ability to effectively manage their performance, making it essential to develop efficient control models. This paper proposes a novel ensemble predictive control model for solar deployments using bio-inspired optimizations to improve load-connected solar deployments’ performance. The proposed model integrates multiple control devices, including Maximum Power Point Tracker, Proportional-Integral-Derivative, Proportional-Integral, and Fuzzy Logic Controllers, to selectively control the solar Photovoltaic systems. The proposed model incorporates a predictive control operation utilizing an LSTM-GRU (Long Short-Term Memory-Gated Recurrent Unit) with the VARMA (Vector Auto-Regressive Moving Average) model, which can accurately predict the future power generation of the solar system. This feature can facilitate efficient energy management and increase the system’s performance for different use cases. Implement a SEPIC (Single Ended Primary Inductor Capacitor) converter design to improve the system’s overall efficiency levels. To validate the effectiveness of the proposed approach, the author conducted experiments using real-world data and compared the proposed results with other control strategies. The results demonstrate that the ensemble predictive control model based on bio-inspired optimizations outperforms the existing control models regarding accuracy, efficiency, and stability levels. The proposed model has the potential to significantly improve the performance of load-connected solar deployments, offering a more practical approach to solar power generation. The combination of predictive control operations with bio-inspired optimizations can facilitate the design of sustainable energy systems with higher efficiency and accuracy.

Assessing the impact of soiling on photovoltaic efficiency using supervised learning techniques

The accumulation of dust and other particles on solar panels, known as soiling, is a significant factor that affects their performance, leading to reduced efficiency if not addressed properly. In this study, we propose a new methodology to estimate soiling on solar photovoltaic panels. To address this issue, we utilised data from the University of Jaén and satellite information from NASA. We applied five different machine learning models, including Linear Regression, Random Forest, Decision Tree, Multilayer Perceptron and Long Short-Term memory neural networks to estimate the extent of soiling on the panels. The input data consisted of weather data, as well as operational data of the solar panels. Our results showed that the MLP model had the lowest average error of 0.0003, indicating its effectiveness in estimating the extent of soiling on the panels. This is significantly lower compared to previous proposals in the literature, which had an average error of 0.026. This study demonstrates the effectiveness of using machine learning methods to forecast soiling on photovoltaic panels accurately. The implications of our findings are essential for optimising energy production and improving the efficiency of solar power systems.

Parameter Extraction of Solar Module Using the Sooty Tern Optimization Algorithm

Photovoltaic module parameter estimation is a critical step in observing, analyzing, and optimizing the efficiency of solar power systems. To find the best value for unknown parameters, an efficient optimization strategy is required. This paper presents the implementation of the sooty tern optimization (STO) algorithm for parameter assessment of a solar cell/module. The simulation findings were compared to four pre-existing optimization algorithms: sine cosine (SCA) algorithm, gravitational search algorithm (GSA), hybrid particle swarm optimization and gravitational search algorithm (PSOGSA), and whale optimization (WOA). The convergence rate and root mean square error evaluations show that the STO method surpasses the other studied optimization techniques. Additionally, the statistical results show that the STO method is superior in average resilience and accuracy. The superior performance and reliability of the STO method are further validated by the Friedman ranking test.

The Influence of Condensing Temperature on the Efficiency of Solar Power Systems with ORC

Organic Rankine cycle technology is suitable for electricity production from low-grade heat sources: solar, geothermal, waste heat. The thermal efficiency of the organic Rankine cycle is influenced the value of the minimum temperature of the cycle, which is the temperature of condensation. The paper analyzes the influence of the condensing temperature on the efficiency of the solar power systems using organic Rankine cycles. Is performed the calculus of thermal efficiency of ORC for condensing temperature in the range 25-10°C. The analyze was done for two different working fluids: R600a and R134a. The results show that the thermal efficiency and amount of electricity produced by a solar ORC system can be increased by lowering the condensing temperature.

Designing of wireless sensor nodes for providing good quality drinking water to the public

Industrialization and urbanization in India have caused heavy environmental pollution. Most of the surface water had been polluted due to the environmental influence. Providing better quality drinking water to public is also a challenge due to pollution in the ground water and contamination even during distribution. Thus, it is very necessary to have adequate methods and equipment for water protection and drinking water quality measurement is an important aspect for the purpose. The protection of public health is an imperative and the potential of millions of severe effects from water contamination is not unrealistic. There is a need of In-situ monitoring, instant collection and calibration of data rather than manual collection of samples and testing. Wireless Sensor Network (WSN) has paved its significance into various applications. Although manual monitoring of water quality has been done, it requires a lot of labor, time and equipment. So, there is a need to develop a robust and reliable smart system where a real time monitoring of parameters of water quality for different water distribution tanks is done all the while. In this paper, Water Quality Monitoring (WQM) in a predefined Wireless Sensor zone using Zigbee Technology is implemented. Water Quality can be accessed in practical systems through the sensors which send the water quality data to the base station. Now a days, Renewable energy power generation is playing a main role in which Solar power is widely used. Maximum Power Point Tracking (MPPT) Controller improves the efficiency of Solar Power System. In this paper, a flexible, reliable Wireless Senor Network (WSN) based method of monitoring the quality of water with maximum power point tracking controlled solar PV system is developed.

A Decision Support Technique to Design Solar Power System for Sindh Agriculture University Tandojam

Pakistan's energy requirements are increasing day by day and not only economic growth but political stability is linked with the availability of energy resources. At present the total generation installed capacity is 21,000 MW. The peak demand is 15,000 MW, but the production is only 9,000-10,000 MW, resulting in 10-12 hours of load shedding. The root cause of the faulty fuel mix is the use of furnace oil as the main fuel to produce thermal electricity. This energy crisis has destroyed at least 50 percent of the industrial sector that has led to increase in the rate of unemployment. It has badly affected the fixed income group because costs of goods and services have increased. Due to the electricity crises a common man look to the available short term alternatives such as generators and Uninterrupted Power Supply (UPS) units but they are not environmental friendly nor have enough storing capacity to provide continuous energy. Due to above problems, the report has been formulated which divert the attention of the policy makers of Sindh Agriculture University Tandojam for bringing the university to the solar system. This process was done using both software and manual analysis, to determine the tilt angle, azimuth degrees, Photovoltaic module size, size of the inverters, batteries and charge controller. At the other hand we also estimated the cost efficiency of solar power system as compare to HESCO in which we analyze the basic appliances like tube lights, savers, fans and computers. The total load of the all faculties and departments are 3, 51,498 Watts per month and the monthly expenditure are paid by university is Rs.11,59,943. But as compare to solar power system the monthly expenditure for same load is just Rs.2, 68,504 and it has a 25 years life as well. If we compare the HESCO and solar power system we would save approximately 76.8% monthly cost. The result of this study can be referred by the policy makers and decision takers of Government and as well as private sectors. We all are causing the problem of pollution, but we need to realize that there are many other power sources for running appliances, and that these options need to be taken advantages.

Research on Photovoltaic System with Supper Capacitor Based on Improved MPPT Algorithm

The output power of photovoltaic (PV) cells would crucially change due to the intensity of solar irradiation and the environment temperature. For the sake of achieving more efficiency of solar power system, the Maximum Power Point Tracking (MPPT) is highly demanded. This paper focuses on analyzing the implemental principle of MPPT with an improved incremental method under the standard conditions. An algorithm is applied to PV system where supper capacitor is chose as energy storage element. Finally, an experimental prototype was designed to verify the validity of the improved increment method. It provides the contrast experiment waveforms between the improved increment method and increment conductance method. The result of experimental shows the effectiveness of the algorithm.

A Solar Automatic Tracking System Design Based on FPGA

In order to improve the solar energy utilization, in the respect of technology, we should perfect solar tracking devices, realization of the sunlight is always vertical to the solar panels. This paper is to design a kind of solar automatic tracking system. Design adopts the traditional photoelectric tracking method, with the FPGA as the core, and by using the methods of scheduled monitoring, achieve precise control of stepping motor, thereby promoting the solar panels rotate remains vertical to the sun, which can effectively improve the efficiency of solar power systems.

퍼지 상태 공간 분할 기법을 이용한 지능형 태양광 추적시스템 설계

태양광을 이용한 태양광 발전시스템은 태양을 정면으로 바라 볼 때 가장 큰 효율을 얻을 수 있다. 즉, 태양의 위치에 대한 집광판(PV; Photovoltic)의 법선벡터를 일치시켜야 가장 높은 효율을 얻게 된다. 본 논문에서는 시간의 변화에 따라 태양의 이동경로를 추적할 수 있도록 태양의 그림자를 판독할 수 있는 8개의 CdS 센서 모듈을 통하여 태양의 위치를 판독하여 태양의 위치를 추적할 수 있는 시스템을 제시하고자 한다. 태양광 추적시스템의 퍼지제어기(fuzzy controller)는 퍼지 입력공간에 대한 격자형 퍼지분할(grid-type fuzzy partition) 영역으로 분할한 후 퍼지규칙(fuzzy rule)을 수립하여 시스템을 제어하도록 설계하였다. 본 논문에서는 태양광 추적을 위한 간단한 모형의 2축 제어시스템을 구성하였으며, CdS 모듈의 좌표축과 집광판의 좌표축을 일치시키도록 구성하였다. 이러한 시스템은 고정된 장소 및 선박과 같은 이동하는 환경에 효과적으로 태양광을 추적할 수 있는 지능형 퍼지제어기의 적용가능 성을 제시하고자 한다. In photovoltaic(PV) system, for obtaining maximum efficiency of solar power systems, the solar tracking system must be controlled to match position of the sun. In this paper, we design the solar tracking system to track movement of the sun using CdS sensor modules and to determine direction of the sun under shadow of directions. In addition, for an intelligent computation in tracking of the sun, a fuzzy controller is allocated to space avaliable for splitting area of fuzzy part for the fuzzy input space(grid-type fuzzy partition) in which a fuzzy grid partition divides fuzzy rules bases. As well, a simple model of solar tracking system is designed by two-axis motor control systems and the 8-direction sensor module that can measure shadow from CdS sensor modules by matching of axis of CdS modules and PV panels. We demonstrate this systems is effective for fixed location and moving vessels and our fuzzy controller can track the satisfactorily.