Mission Profile Data Research Articles

Reliability oriented performance evaluation of PV inverter with bifacial panels considering albedos

The recent advancements in the solar photovoltaic technology is bifacial panels. These panels are capable of producing higher energy than their conventional panels by capturing from both front and rear sides. By harvesting solar energy from both the front and rare surfaces of the panels, the load on the inverters can increase. This affects its reliability performance. Nevertheless, inverter is reported as the critical component in the photovoltaic (PV) system. Hence this work presents reliability-oriented performance evaluation of PV inverter with bifacial panels is proposed. A 3-kilowatt photovoltaic system has been considered with yearly mission profile data at Hyderabad, India. This evaluation is carried out under various albedos. Finally, a comparison between monofacial and bifacial PV panel are presented. The results show that the albedo significantly impacts the lifetime of a PV inverter and therefore, the albedo should be considered when designing a bifacial panel's inverter.

Mission profile-based assessment of photovoltaic system reliability for Indian climatic zones

ABSTRACT Solar energy is expected to overtake wind energy as the leading renewable energy source by 2050, and therefore, accurate lifetime and reliability predictions for solar photovoltaic (PV) installations are necessary. The microinverter is a pivotal component of stand-alone PV power systems. The reliability of the microinverter depends on the mission profile of the geographical location, such as solar irradiance, ambient temperature, and wind velocity. This study developed a comprehensive reliability map for a 200Wp monocrystalline panel and a 250W microinverter based on mission profile data collected from 198 locations across India. A novel mathematical model is developed and validated using Ansys Icepak to evaluate the junction temperatures of microinverter components. The results show that the junction temperature significantly affects the reliability of PV system. In the hot and dry climatic zone of Jaisalmer, Rajasthan, the maximum MOSFET junction temperature and Mean Time Between Failure (MTBF) obtained were 63.14°C, and 27.34 years, respectively. Furthermore, in the cold and dry climatic zone of Leh, Ladakh, the junction temperature dropped to 29.74°C, increasing MOSFET’s MTBF to 49.8 years. These results prove that the reliability depends on the mission profile, and it should not be ignored in evaluating the reliability of solar PV systems.

A Reliability-Optimized Maximum Power Point Tracking Algorithm Utilizing Neural Networks for Long-Term Lifetime Prediction for Photovoltaic Power Converters

The reliability of power converters in photovoltaic systems is critical to the overall system reliability. This paper proposes a novel active thermal-controlled algorithm that aims to reduce the rate of junction temperature increase, therefore, increasing the reliability of the device. The algorithm works alongside a normal perturb and observe maximum power point tracking algorithm, taking control when certain temperature criteria are met. In conjunction with a neural network, the algorithm is applied to long-term real mission profile data. This would grant a better understanding of the real-world trade-offs between energy generated and lifetime improvement when using the proposed algorithm, as well as shortening study cycle times. The neural network, when applied to 365 days of data, was 28 times faster than using standard electrothermal modeling, and the lifetime consumption was predicted with greater than 96.5% accuracy. Energy generated was predicted with greater than 99.5% accuracy. The proposed algorithm resulted in a 3.3% reduction in lifetime consumption with a 1.0% reduction in the total energy generated. There is a demonstrated trade-off between lifetime consumption reduction and energy-generated reduction. The results are also split by environmental conditions. Under very variable conditions, the algorithm resulted in a 4.4% reduction in lifetime consumption with a 1.4% reduction in the total energy generated.

Reliability evaluation of carrier‐based pulse width modulated three‐level F‐type neutral point clamped inverter with power loss analysis

AbstractAn improved carrier‐based pulse‐width‐modulation (CBPWM) technique for a three‐level (3L) F‐type neutral point‐clamped (FNPC) inverter is presented in this paper. By using the proposed modulation scheme, the DC‐link capacitor voltage at the neutral point is balanced. For manufacturers, reliability is one of the major concerns for all power electronics devices and parts. This paper proposes an extensive reliability analysis of the 3L‐FNPC inverter. Considering the mean time to failure (MTTF) of each device, the reliability of the 3L‐FNPC inverter is analyzed in this present work by using two different methods, which are the approximate method and the exact method. To compute the failure rate of the equipment, only the number of components is considered in the approximate method. Alternatively, exact methods are utilized to evaluate the reliability of a system by knowing the different stress levels and various operating environments of a specific system. Because of the strong correlation between device failure and temperature stress factors, power loss analysis is used in the exact method. The MATLAB Simulink environment is used to determine the power loss of the switching devices. A solar off‐grid photovoltaic (PV) system is considered for reliability testing. Experimental mission profile data are used to estimate the lifetime of the 3L‐FNPC inverter by applying the rainflow counting algorithm, the Coffin‐Manson model, and Miner's rule. The competitiveness, effectiveness, and reliability of the 3L‐FNPC inverter with the proposed modulation technique are validated using a hardware prototype.

Estimation of remaining useful lifetime of power electronic components with machine learning based on mission profile data

Estimation of remaining useful lifetime of power electronic components with machine learning based on mission profile data

Estimation of Remaining Useful Lifetime of Power Electronic Components with Machine Learning Based on Mission Profile Data

Estimation of Remaining Useful Lifetime of Power Electronic Components with Machine Learning Based on Mission Profile Data

Comparative Study of a Powerplant Life Consumption Rate When Installed in Two Different Aircraft Variants

The Hellenic Air Force (HAF) operates both EMB-145 and EMB-135 LR versions of Embraer aircraft, used in surveillance and civil missions respectively. These aircraft are equipped with the same version of Rolls Royce, AE 3007 turbofan engine. This study aims to quantify and compare the life consumption rate of this engine when installed in each of the two aircraft variants. Two typical missions, one for each variant, were constructed based on mission profile data dictated by the aircraft commanders. For each mission profile segment, corresponding engine data were matched out of the engine recordings archives held by the Hellenic Air Force. The life consumption rate was based on the Low Cycle Fatigue (LCF) and creep cumulative detrimental effect on the rotor blades of the 1st High-Pressure Turbine stage. For the LCF, the rainflow method was used to determine the respective loading cycles, whereas the Larson - Miller parameter method was used to determine the consumed life fractions due to creep. The main conclusion of the study was that the engine when installed in the EMB-145 military variant, is much more loaded. Despite the fact absolute life consumption values could hide a great level of uncertainty, the comparative outcomes wherein errors are, to a certain extent, cancelled out, could be used as a rule of thumb when monitoring engine life consumption rates.

Impact of Solar Irradiance and Ambient Temperature on PV Inverter Reliability Considering Geographical Locations

Today inverter system is one of the enabling technologies for efficiently harnessing energy from renewable energy sources (Solar, Wind, etc.,) and also for high reliable grid interfacing systems. With the advancements in power electronics, inverter conversion efficiency pushed to 98%, also PV is becoming a major renewable energy source globally. Nevertheless, the reliability performance of PV inverter is of high concern. Different environmental factors like solar irradiance, ambient temperature (also called Mission Profile) affect the reliability performance of PV inverter. Environmental conditions vary from location to location. Hence to quantify the reliability performance of PV inverter all these factors need to be considered. In this paper reliability performance of PV inverter is evaluated considering environmental factors and geographical locations. For the reliability evaluation, a 1-ϕ, 3-kW grid connected PV system is developed in PLECS. Full bridge PV inverter with 600V/30A IGBT is employed as the interface between grid and PV source. Real time mission profile data of one-year logs at India (Relatively hot climate) and Denmark (Relatively cold climate) to account for environmental factors and geographical locations during the reliability performance evaluation of PV inverter. Monte Carlo simulation is used to generate a population of 20000 samples with 5% variation. Lifetime for 20000 samples is calculated and fitted in Two Parameter Weibull distribution. B10 lifetime is calculated at two locations. The results of this paper reveal that environmental factors and geographical locations have a significant impact on PV inverter reliability performance.

Estimation of Lifetime and Reliability of Input Rectifier in Variable Frequency Drives

This research paper investigates the causes of power module failures in front-end rectifier of variable frequency drive (VFD) and their lifetime estimation. The front-end rectifier plays an important role in the electrical system for energy conversion. However, from the industrial survey it is found that they are sensitive to harsh operating conditions and are prone to damage. Hence, it becomes inevitable to analyse the causes of their failure and lifetime. Due to prolonged usage, sudden variations in supply and load, the power modules will be subjected to stresses such as overvoltage, voltage variations due to sag, phase unbalance, over current, temperature, harmonics and pre-distortions. The major causes of failure are due to several stresses such as solder joint break, bond wire lift-off, cracks in power modules are discussed. In this paper, the lifetime of the power module is estimated for a given mission profile data. The junction temperature profile is obtained from the VFD Front-end rectifier simulation model. The number of temperature cycles from the temperature profile is estimated using cycle counting methods. Lifetime estimation and damage modelling is done using Coffin-Manson rule and Palmgren Miner rules. All these modelling and simulations are done for a specific VFD power size using MATLAB and PLECS.

Reliability-Oriented Design of Modular Multilevel Converters for Medium-Voltage STATCOM

Modular multilevel converters (MMC) are complex systems, composed of many elements, and exposed to critical load demands in some cases. Thereby, a detailed design of its components is of preeminent importance to achieve a high system-level reliability. However, the high number of devices challenges the tradeoff between cost and reliability. This article, introduces a reliability-oriented design methodology, based on the cost to achieve a predefined unreliability level ( $U_x$ ). A flowchart presents the main steps of the process, including the mission profile definition, selection of power devices, thermal modeling, reliability modeling, and the reliability-oriented selection. To evaluate the proposed methodology, a case study considering 17 MVA/13.8 kV MMC-static synchronous compensator (STATCOM) with a real mission profile data is conducted. A $U_x - cost$ map is introduced to compare various design solutions, based on power devices of different voltage classes and current capabilities.

Predictive algorithm to determine the suitable time to change automotive engine oil

Recently, emerging technologies related to various sensors, product identification, and wireless communication give us new opportunities for improving the efficiency of automotive maintenance operations, in particular, implementing predictive maintenance. The key point of predictive maintenance is to develop an algorithm that can analyze degradation status of automotive and make predictive maintenance decisions. In this study, as a basis for implementing the predictive maintenance of automotive engine oil, we propose an algorithm to determine the suitable change time of automotive engine oil by analyzing its degradation status with mission profile data. For this, we use several statistical methods such as factor analysis, discriminant and classification analysis, and regression analysis. We identify main factors of mission profile and engine oil quality with factor analysis. Subsequently, with regression analysis, we specify relations between main factors considering the types of mission profile of automotive: urban-mode and highway-mode. Based on them, we determine the proper change time of engine oil through discriminant and classification analysis. To evaluate the proposed approach, we carry out a case study and have discussion about limitations of our approach.

Real time hardware-in-the-loop simulation verifies performance of Gemini computer and operational program

An IBM Gemini Mission Verification Simulation (MVS) facility exercises and verifies the performance of the actual Gemini digital computer and its operational programs through real time, hardware-in-the-loop simulations of Gemini missions. This paper gives a system-type functional description of MVS and describes the methodology used to verify performance of the actual computer through com parisons and analyses of mission profile data generated both by MVS and all-digital reference simulations. A de scription of the various types of mission profile data ob tained together with a discussion of its significance and use is included. The error analysis techniques used to establish the expected deviations between mission profile data generated by MVS and the reference all-digital simu lations are described.