Integrated predictive control and fault diagnosis algorithm for single inductor-based DC-DC converters for photovoltaic systems

Circuit World ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 105-116
Author(s):  
Venkateswaran M. ◽  
Govindaraju C. ◽  
Santhosh T.K.

Purpose Power converters are an integral part of the energy conversion process in solar photovoltaic (PV) systems which is used to match the solar PV generation with the load requirements. The increased penetration of renewable invokes intermittency in the generated power affecting the reliability and continuous energy supply of such converters. DC-DC converters deployed in solar PV systems impose stringent restrictions on supplied power, continuous operation and fault prediction scenarios by continuously observing state variables to ensure continuous operation of the converter. Design/methodology/approach A converter deployed for a mission-critical application has to ensure continuous regulated output for which the converter has to ensure fault-free operation. The fault diagnostic algorithm relies on the measurement of a state variable to assess the type of fault. In the same line, a predictive controller depends on the measurement of a state variable to predict the control variable of a converter system to regulate the converter output around a fixed or a variable reference. Consequently, both the fault diagnosis and the predictive control algorithms depend on the measurement of a state variable. Once measured, the available data can be used for both algorithms interchangeably. Findings The objective of this work is to integrate the fault diagnostic and the predictive control algorithms while sharing the measurement requirements of both these control algorithms. The integrated algorithms thus proposed could be applied to any converter with a single inductor in its energy buffer stage. Originality/value laboratory prototype is created to verify the feasibility of the integrated predictive control and fault diagnosis algorithm. As the proposed method combine the fault detection algorithm along with predictive control, a load step variation and manual fault creation methods are used to verify the feasibility of the converter as with the simulation analysis. The value for the capacitors and inductors were chosen based on the charge-second and volt-second balance equations obtained from the steady-state analysis of boost converter.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anas Sani Maihulla ◽  
Ibrahim Yusuf ◽  
Muhammad Salihu Isa

PurposeSolar photovoltaic (PV) is commonly used as a renewable energy source to provide electrical power to customers. This research establishes a method for testing the performance reliability of large grid-connected PV power systems. Solar PV can turn unrestricted amounts of sunlight into energy without releasing carbon dioxide or other contaminants into the atmosphere. Because of these advantages, large-scale solar PV generation has been increasingly incorporated into power grids to meet energy demand. The capability of the installation and the position of the PV are the most important considerations for a utility company when installing solar PV generation in their system. Because of the unpredictability of sunlight, the amount of solar penetration in a device is generally restricted by reliability constraints. PV power systems are made up of five PV modules, with three of them needing to be operational at the same time. In other words, three out of five. Then there is a charge controller and a battery bank with three batteries, two of which must be consecutively be in operation. i.e. two out of three. Inverter and two distributors, all of which were involved at the same time. i.e. two out of two. In order to evaluate real-world grid-connected PV networks, state enumeration is used. To measure the reliability of PV systems, a collection of reliability indices has been created. Furthermore, detailed sensitivity tests are carried out to examine the effect of various factors on the efficiency of PV power systems. Every module's test results on a realistic 10-kW PV system. To see how the model works in practice, many scenarios are considered. Tables and graphs are used to show the findings.Design/methodology/approachThe system of first-order differential equations is formulated and solved using Laplace transforms using regenerative point techniques. Several scenarios were examined to determine the impact of the model under consideration. The calculations were done with Maple 13 software.FindingsThe authors get availability, reliability, mean time to failure (MTTF), MTTF sensitivity and gain feature in this research. To measure the reliability of PV systems, a collection of reliability indices has been created. Furthermore, detailed sensitivity tests are carried out to examine the effect of various factors on the efficiency of PV power systems.Originality/valueThis is the authors' original copy of the paper. Because of the importance of the study, the references are well-cited. Nothing from any previously published articles or textbooks has been withdrawn.


2020 ◽  
Vol 12 (5) ◽  
pp. 2011 ◽  
Author(s):  
Sufyan Samara ◽  
Emad Natsheh

The expanding use of photovoltaic (PV) systems as an alternative green source for electricity presents many challenges, one of which is the timely diagnosis of faults to maintain the quality and high productivity of such systems. In recent years, various studies have been conducted on the fault diagnosis of PV systems. However, very few instances of fault diagnostic techniques could be implemented on integrated circuits, and these techniques require costly and complex hardware. This work presents a novel and effective, yet small and implementable, fault diagnosis algorithm based on an artificial intelligent nonlinear autoregressive exogenous (NARX) neural network and Sugeno fuzzy inference. The algorithm uses Sugeno fuzzy inference to isolate and classify faults that may occur in a PV system. The fuzzy inference requires the actual sensed PV system output power, the predicted PV system output power, and the sensed surrounding conditions. An artificial intelligent NARX-based neural network is used to obtain the predicted PV system output power. The actual output power of the PV system and the surrounding conditions are obtained in real-time using sensors. The algorithm is proven to be implementable on a low-cost microcontroller. The obtained results indicate that the fault diagnosis algorithm can detect multiple faults such as open and short circuit degradation, faulty maximum power point tracking (MPPT), and conditions of partial shading (PS) that may affect the PV system. Moreover, radiation and temperature, among other non-linear associations of patterns between predictors, can be captured by the proposed algorithm to determine the accurate point of the maximum power for the PV system.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mars Nadia ◽  
Houcine Lassad ◽  
Zaafouri Abderrahmen ◽  
Chaari Abdelkader

Abstract Tunisia has high solar radiation levels, which makes it suitable for the installation of photovoltaic (PV) systems. The design of these kinds of systems is an important step because there are many crucial factors to assess the PV module efficiency such as temperature, module types and solar radiation.This paper aims to give an analysis of the most influencing factor for selecting location. In fact, after estimating the PV panel inclination, the solar radiation and the temperature in “Zarzis” (southeastern of Tunisia), a comparative analysis among the different PV panel types was given. Additionally, to find which technologies are suitable for the climate conditions of this area, it is important to compare the effect of temperature and solar radiation on their performances.


Author(s):  
Aruoriwoghene Okere ◽  
M. Tariq Iqbal

This paper reviews various faults that exist in large solar Photovoltaic (PV) systems. The faults are reviewed in their various classes based on the location and structure. Conventional solutions for fault detection and various research work in PV system monitoring and fault detection are reviewed. It is obvious that PV module level monitoring exhibit advantages over array or string monitoring. Therefore, the paper proposes the use of Long Range (LoRa) Wireless Sensor Networks (WSN) for PV module level monitoring and fault diagnosis. LoRa was proposed for this application due to the advantages it has over other wireless technologies which include long range of data transfer, low cost, low power consumption and multi sensor connection capabilities.


2019 ◽  
Vol 8 (1) ◽  
pp. 34-52 ◽  
Author(s):  
M. Asif ◽  
Mohammad A. Hassanain ◽  
Kh Md Nahiduzzaman ◽  
Haitham Sawalha

Purpose The Kingdom of Saudi Arabia (KSA) is facing a rapid growth in energy demand mainly because of factors like burgeoning population, economic growth, modernization and infrastructure development. It is estimated that between 2000 and 2017 the power consumption has increased from 120 to 315 TWh. The building sector has an important role in this respect as it accounts for around 80 percent of the total electricity consumption. The situation is imposing significant energy, environmental and economic challenges for the country. To tackle these problems and curtail its dependence on oil-based energy infrastructure, KSA is aiming to develop 9.5 GW of renewable energy projects by 2030. The campus of the King Fahd University of Petroleum and Minerals (KFUPM) has been considered as a case study. In the wake of recently announced net-metering policy, the purpose of this paper is to investigate the prospects of rooftop application of PV in buildings. ArcGIS and PVsyst software have been used to determine the rooftop area and undertake PV system modeling respectively. Performance of PV system has been investigated for both horizontal and tilted installations. The study also investigates the economic feasibility of the PV application with the help of various economic parameters such as benefit cost ratio, simple payback period (SPP) and equity payback periods. An environmental analysis has also been carried out with the help of RETScreen software to determine the savings in greenhouse gas emissions as a result of PV system. Design/methodology/approach This study examines the buildings of the university campus for utilizable rooftop areas for PV application. Various types of structural, architectural and utilities-related features affecting the use of building roofs for PV have been investigated to determine the corrected area. To optimize the performance of the PV system as well as space utilization, modeling has been carried out for both horizontal and tilted applications of panels. Detailed economic and environmental assessments of the rooftop PV systems have also been investigated in detail. Modern software tools such as PVsyst, ArcGIS and RETScreen have also been used for system design calculations. Findings Saudi Arabia is embarking on a massive solar energy program as it plans to have over 200 GW of installed capacity by 2030. With solar energy being the most abundant of the available renewable resource for the country, PV is going to be one of the main technologies in achieving the set targets. The country has, however, unlike global trends, traditionally overlooked the small-scale and building-related application of solar PV, focusing mainly on larger projects. This study explores the prospects of utilization of solar PV on building roofs. Building rooftops are constrained in terms of PV application owing to wide ranging obstacles that can be classified into five types – structural, services, accessibility, maintenance and others. The total building rooftop area in the study zone, calculated through ArcGIS has been found to be 857,408 m2 of which 352,244 m2 is being used as car parking and hence is not available for PV application. The available roof area, 505,165 m2 is further hampered by construction and utilities related features including staircases, HVAC systems, skylights, water tanks and satellite dish antennas. Taking into account the relevant obstructive features, the net rooftop area covered by PV panels has been found to be in the range 25–41 percent depending upon the building typology, with residential buildings offering the least. To optimize both the system efficiency and space utilization, PV modeling has been carried out with the help of PVsyst software for both the tilted and horizontal installations. In terms of output, PV panels with tilt angle of 24° have been found to be 9 percent more efficient compared to the horizontally installed ones. Modeling results provide a net annual output 37,750 and 46,050 MWh from 21.44 and 28.51 MW of tilted and horizontal application of PV panels, sufficient to respectively meet 16 and 20 percent of the total campus electricity requirements. Findings of the economic analysis reveal the average SPP for horizontal and tilted applications of the PV to be 9.2 and 8.4 years, respectively. The benefit cost ratio for different types of buildings for horizontal and tilted application has been found to be ranging between 0.89 and 2.08 and 0.83 and 2.15, respectively. As electricity tariff in Saudi Arabia has been increased this year by as much as 45 percent and there are plans to remove $54bn of subsidy by 2020, the cost effectiveness of PV systems will be greatly helped. Application of PV in buildings can significantly improve their environmental performance as the findings of this study reveal that the annual greenhouse gas emission in the KFUPM campus can be reduced by as much as 40,199 tons carbon dioxide equivalent. Originality/value The PV application on building roof especially from economic perspective is an area which has not been addressed thus far. Khan et al. (2017) studied the power generation potential for PV application on residential buildings in KSA. Asif (2016) also investigated power output potential of PV system in different types of buildings. Dehwas et al. (2018) adopted a detailed approach to determine utilizability of PV on residential building roofs. None of these studies have covered the economics of PV systems. This study attempts to address the gap and contribute to the scholarship on the subject. It targets to determine the power output from different types of building in an urban environment by taking into account building roof conditions. It also provides detailed economic assessment of PV systems. Subsequent environmental savings are also calculated.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bo Wang ◽  
Guanwei Wang ◽  
Youwei Wang ◽  
Zhengzheng Lou ◽  
Shizhe Hu ◽  
...  

Purpose Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults. Design/methodology/approach This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced. Findings This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods. Originality/value This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.


2018 ◽  
Vol 1 (3) ◽  
Author(s):  
IJE Manager

In the past century, fossil fuels have dominated energy supply in Indonesia. However, concerns over emissions are likely to change the future energy supply. As people become more conscious of environmental issues, alternatives for energy are sought to reduce the environmental impacts. These include renewable energy (RE) sources such as solar photovoltaic (PV) systems. However, most RE sources like solar PV are not available continuously since they depend on weather conditions, in addition to geographical location. Bali has a stable and long sunny day with 12 hours of daylight throughout the year and an average insolation of 5.3 kWh/m2 per day. This study looks at the potential for on-grid solar PV to decarbonize energy in Bali. A site selection methodology using GIS is applied to measure solar PV potential. Firstly, the study investigates the boundaries related to environmental acceptability and economic objectives for land use in Bali. Secondly, the potential of solar energy is estimated by defining the suitable areas, given the technical assumptions of solar PV. Finally, the study extends the analysis to calculate the reduction in emissions when the calculated potential is installed. Some technical factors, such as tilting solar, and intermittency throughout the day, are outside the scope of this study. Based on this model, Bali has an annual electricity potential for 32-53 TWh from solar PV using amorphous thin-film silicon as the cheapest option. This potential amount to three times the electricity supply for the island in 2024 which is estimated at 10 TWh. Bali has an excessive potential to support its own electricity demand with renewables, however, some limitations exist with some trade-offs to realize the idea. These results aim to build a developmental vision of solar PV systems in Bali based on available land and the region’s irradiation.


Author(s):  
Dan Bodoh ◽  
Anthony Blakely ◽  
Terry Garyet

Abstract Since failure analysis (FA) tools originated in the design-for-test (DFT) realm, most have abstractions that reflect a designer's viewpoint. These abstractions prevent easy application of diagnosis results in the physical world of the FA lab. This article presents a fault diagnosis system, DFS/FA, which bridges the DFT and FA worlds. First, it describes the motivation for building DFS/FA and how it is an improvement over off-the-shelf tools and explains the DFS/FA building blocks on which the diagnosis tool depends. The article then discusses the diagnosis algorithm in detail and provides an overview of some of the supporting tools that make DFS/FA a complete solution for FA. It also presents a FA example where DFS/FA has been applied. The example demonstrates how the consideration of physical proximity improves the accuracy without sacrificing precision.


2009 ◽  
Vol 20 (9) ◽  
pp. 2520-2530
Author(s):  
Ling-Wei CHU ◽  
Shi-Hong ZOU ◽  
Shi-Duan CHENG ◽  
Chun-Qi TIAN ◽  
Wen-Dong WANG

Sign in / Sign up

Export Citation Format

Share Document