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- All Subjects: microinverter
- Creators: Qin, Jiangchao
- Member of: ASU Electronic Theses and Dissertations
Description
Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved.
ContributorsLuo, Shuman (Author) / Weng, Yang (Thesis advisor) / Lei, Qin (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
The demand for cleaner energy technology is increasing very rapidly. Hence it is
important to increase the eciency and reliability of this emerging clean energy technologies.
This thesis focuses on modeling and reliability of solar micro inverters. In
order to make photovoltaics (PV) cost competitive with traditional energy sources,
the economies of scale have been guiding inverter design in two directions: large,
centralized, utility-scale (500 kW) inverters vs. small, modular, module level (300
W) power electronics (MLPE). MLPE, such as microinverters and DC power optimizers,
oer advantages in safety, system operations and maintenance, energy yield,
and component lifetime due to their smaller size, lower power handling requirements,
and module-level power point tracking and monitoring capability [1]. However, they
suer from two main disadvantages: rst, depending on array topology (especially
the proximity to the PV module), they can be subjected to more extreme environments
(i.e. temperature cycling) during the day, resulting in a negative impact to
reliability; second, since solar installations can have tens of thousands to millions of
modules (and as many MLPE units), it may be dicult or impossible to track and
repair units as they go out of service. Therefore identifying the weak links in this
system is of critical importance to develop more reliable micro inverters.
While an overwhelming majority of time and research has focused on PV module
eciency and reliability, these issues have been largely ignored for the balance
of system components. As a relatively nascent industry, the PV power electronics
industry does not have the extensive, standardized reliability design and testing procedures
that exist in the module industry or other more mature power electronics
industries (e.g. automotive). To do so, the critical components which are at risk and
their impact on the system performance has to be studied. This thesis identies and
addresses some of the issues related to reliability of solar micro inverters.
This thesis presents detailed discussions on various components of solar micro inverter
and their design. A micro inverter with very similar electrical specications in
comparison with commercial micro inverter is modeled in detail and veried. Components
in various stages of micro inverter are listed and their typical failure mechanisms
are reviewed. A detailed FMEA is conducted for a typical micro inverter to identify
the weak links of the system. Based on the S, O and D metrics, risk priority number
(RPN) is calculated to list the critical at-risk components. Degradation of DC bus
capacitor is identied as one the failure mechanism and the degradation model is built
to study its eect on the system performance. The system is tested for surge immunity
using standard ring and combinational surge waveforms as per IEEE 62.41 and
IEC 61000-4-5 standards. All the simulation presented in this thesis is performed
using PLECS simulation software.
important to increase the eciency and reliability of this emerging clean energy technologies.
This thesis focuses on modeling and reliability of solar micro inverters. In
order to make photovoltaics (PV) cost competitive with traditional energy sources,
the economies of scale have been guiding inverter design in two directions: large,
centralized, utility-scale (500 kW) inverters vs. small, modular, module level (300
W) power electronics (MLPE). MLPE, such as microinverters and DC power optimizers,
oer advantages in safety, system operations and maintenance, energy yield,
and component lifetime due to their smaller size, lower power handling requirements,
and module-level power point tracking and monitoring capability [1]. However, they
suer from two main disadvantages: rst, depending on array topology (especially
the proximity to the PV module), they can be subjected to more extreme environments
(i.e. temperature cycling) during the day, resulting in a negative impact to
reliability; second, since solar installations can have tens of thousands to millions of
modules (and as many MLPE units), it may be dicult or impossible to track and
repair units as they go out of service. Therefore identifying the weak links in this
system is of critical importance to develop more reliable micro inverters.
While an overwhelming majority of time and research has focused on PV module
eciency and reliability, these issues have been largely ignored for the balance
of system components. As a relatively nascent industry, the PV power electronics
industry does not have the extensive, standardized reliability design and testing procedures
that exist in the module industry or other more mature power electronics
industries (e.g. automotive). To do so, the critical components which are at risk and
their impact on the system performance has to be studied. This thesis identies and
addresses some of the issues related to reliability of solar micro inverters.
This thesis presents detailed discussions on various components of solar micro inverter
and their design. A micro inverter with very similar electrical specications in
comparison with commercial micro inverter is modeled in detail and veried. Components
in various stages of micro inverter are listed and their typical failure mechanisms
are reviewed. A detailed FMEA is conducted for a typical micro inverter to identify
the weak links of the system. Based on the S, O and D metrics, risk priority number
(RPN) is calculated to list the critical at-risk components. Degradation of DC bus
capacitor is identied as one the failure mechanism and the degradation model is built
to study its eect on the system performance. The system is tested for surge immunity
using standard ring and combinational surge waveforms as per IEEE 62.41 and
IEC 61000-4-5 standards. All the simulation presented in this thesis is performed
using PLECS simulation software.
ContributorsManchanahalli Ranganatha, Arkanatha Sastry (Author) / Ayyanar, Raja (Thesis advisor) / Karady, George G. (Committee member) / Qin, Jiangchao (Committee member) / Arizona State University (Publisher)
Created2015