Reliability of Candidate Photovoltaic Module-Integrated-Inverter (PV-MII) Topologies—A Usage Model Approach

Harb, Souhib; Balog, Robert S.

doi:10.1109/tpel.2012.2222447

Cited by 174 publications

(54 citation statements)

References 20 publications

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“…Moreover, DMPPT architectures require further components (e.g., large electrolytic capacitances) that may reduce the system reliability and hence its lifetime. In addition, the high thermal stresses due to the installation of the switching converters close to the PV module may shorten their lifetime (91). An alternative solution capable to mitigate the drawbacks associated to mismatching operating conditions is represented by the dynamical reconfiguration of PV arrays by means of a reconfigurable switch matrix (made of MOSFETs or of electromechanical relays), which allows changing the connections of the PV modules (92)(93)(94)(95)(96)(97)(98)(99)(100)(101)(102)(103)(104)(105)(106).…”

Section: Reconfiguration Techniquesmentioning

confidence: 99%

Maximum Power Point Tracking Techniques

Balato

Costanzo

Vitelli

2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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An essential objective for many photovoltaic (PV) applications is represented by the so‐called maximum power point tracking (MPPT). Under uniform irradiance and ambient temperature conditions, the power versus voltage characteristic of a PV array is characterized by the presence of a unique point, termed maximum power point (MPP), where the power provided by the array itself attains its maximum value. Any other operating point leads to the extraction of a smaller PV energy. Therefore, in order to avoid the waste of PV energy, the working of the array in its MPP is mandatory. Since the position of the MPP continuously changes in time as a function of irradiance and temperature, it is necessary to continuously track it. Such an objective can be obtained by using proper MPPT control techniques. A large number of papers have appeared in the scientific literature during the last 15 years on the issue of MPPT, and as a result it may be difficult to decide the best technique for a given application. Therefore, this survey may hopefully be beneficial to students, researchers, and practitioners in PV systems. The article overviews the representative PV MPPT algorithms, organized into four main groups. The first group contains the so‐called “hill climbing” techniques, the second group contains the “curve fitting” techniques, the third group contains the “dynamical” techniques, and the fourth group includes all the techniques that do not belong to the first three groups.

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Section: Reconfiguration Techniquesmentioning

confidence: 99%

Maximum Power Point Tracking Techniques

Balato

Costanzo

Vitelli

2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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“…However, results of CA on BoS, presented in Figure 3, show that PVI contributes significantly in the failures of BoS. Meanwhile, the main concern of the current studies is quite limited to reliability estimations of PVI and reliability improvements of current PVI [30]- [34].…”

Section: Criticality Analysis Of Pv Invertermentioning

confidence: 94%

A root cause analysis and a risk evaluation of PV balance of system failures

2017

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<p>The Photovoltaic (PV) system is divided mainly into two subsystems; PV modules and a Balance of System (BoS) subsystems. This work shows two approaches for a reliability analysis on the subsystem level of aBoS: Failure mode effects criticality analysis (FMECA) and a Markov Process. FMECA concerns the root causes of failures and introduces prioritization numbers to highlight critical components of a BoS. Meanwhile, a Markov process is a reliability methodology that aims to predict the probability of success and failure of a BoS. In this way, a Markov process is a supportive tool for helping decision-makers to judge the criticality of failures associated with the operation of PV systems. Results show that the PV inverter contributes significantly to the failures of a BoS. Accordingly, further investigations are conducted on a PV inverter to prioritize the maintenance activities by determining the risk priority number of its component failures through quantitative CA. The novelty of the proposed methodologies stems from analyzing the roots of failure causes of BoS components and estimating the probability of failure of these components in order to improve the early development of a BoS, enhance maintenance management, and satisfy the demanding reliability by electric utilities.</p>

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“…As far as photovoltaic inverter (PVI) components are concerned, the practical implementation of the 'Design for inverter reliability' would allow an enhancement of the electrical and electronics devices/systems performance. Recent interesting scientific papers have been investigated the PVI reliability/availability aspects: some papers are specifically devoted to a PVI design 'reliability-oriented' [8][9][10], others to propose attractive methodologies for estimating their reliability during the operating [11][12][13]. The need to manage the uncertain reliability data calls for more robust and selective methods, aimed at evaluating the reliability and availability parameters at the design stage and at checking the expected data in the process of manufacturing or during all the operational life-time.…”

Section: Introductionmentioning

confidence: 99%

Availability analysis of photovoltaic inverters in presence of uncertain data via Bayesian approach

Chiodo¹,

Lauria²,

Pisani³

2015

IET Generation, Transmission & Distribution

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The paper presents an advanced Bayesian technique to carry out availability analyses of photovoltaic inverters in presence of uncertain reliability data. In order to proper take into account the above uncertainty, photovoltaic inverter hazard rate and repair rate are considered as random variables characterized by proper Log-normal and/or Gamma distributions. These assumptions are adequately motivated by well known and established physical and mathematical considerations. In this manner the photovoltaic availability uncertainty can be expressed as a function of the component uncertain data. Afterwards, thanks to the tailored Bayesian technique we demonstrate how, even in presence of scarce data, an efficient updating of the system performance can be accomplished during the photovoltaic inverter operating life. The methodology is really useful in view of an optimal allocation of the components’ reliabilities at the design stage and in order to establish the actual expected payback of the whole photovoltaic system. Extensive numerical simulations confirm the effectiveness and the validity of the developed Bayesian technique by also verifying its inherent robustness and efficiency through a rigorous analysis

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Reliability of Candidate Photovoltaic Module-Integrated-Inverter (PV-MII) Topologies—A Usage Model Approach

Cited by 174 publications

References 20 publications

Maximum Power Point Tracking Techniques

Maximum Power Point Tracking Techniques

A root cause analysis and a risk evaluation of PV balance of system failures

Availability analysis of photovoltaic inverters in presence of uncertain data via Bayesian approach

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