Wind power forecasting and its applications to the power system

textThe goal of research in this dissertation is to bring more wind resources into the power grid by mitigating the uncertainty of the current wind power, by developing a new algorithm to respond to the fluctuation of the future wind power, and by building additional transmission lines to bring more wind resources from a remote area to the load center. First, in order to overcome the wind power uncertainty, the probabilistic and ensemble wind power forecasting is proposed to increase the forecasting accuracy and to deliver the probability density function of the uncertainty. Accurate wind power forecasting reduces the amounts and cost of ancillary services (AS). As the mismatch between the bid and actual amount of delivered energy decreases, the imbalance between supply and demand also decreases. If the forecasting ahead is increased up to 24 hours, accurate wind power forecasting can also help wind farm owners bid the exact amount of wind power in the day ahead (DA) market. Furthermore, wind power owners can use the parametric probabilistic density of error distributions for hedging the price risk and building a better offer curve. Second, a novel algorithm to generate many wind power scenarios as a function of installed capacity of wind power is proposed based on an analysis of the power spectral density of wind power. Scenarios can be used to simulate the power system to estimate the required amount of AS to respond to the fluctuation of future wind power as the installed capacity of wind power increases. Scenarios have statistical characteristics of the future wind power that are regressed as a function of the installed capacity of wind power from the statistical characteristics of the current wind power. This algorithm can generate many possible scenarios to simulate the power system in many different situations. Third, optimal transmission expansion by simulating the power system with the multiple load and wind power scenarios in different locations is planned to prepare the preliminary result to bring more wind resources in remote areas to the load center in Texas. In this process, the geographical smoothing effects of wind power and the stochastic correlation structure between the load and wind power are considered. Furthermore, the generalized dynamic factor model (GDFM) is used to synthesize load and wind power scenarios to keep their correlation structure. The premise of the GDFM is that a few factors can drive the correlated movements of load and wind power simultaneously, so the scenario generation process is parsimonious.Electrical and Computer Engineerin

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed

Design and implementation of three-phase inverters using a TMS320F2812 digital signal processor

textThe goal of this thesis project was to design and build a three-phase inverter controlled by the TMS320F2812 DSP by Texas Instruments. The TMS320F2812 is controlled in order to make inverters generate output waveforms which mimic the main reference signal coming from a computer. The project included building three different inverters on two platforms including auxiliary circuits and designing five pulse width modulation (PWM) switching algorithms for the inverters. The motivation was that a newly designed inverter was required as an intermediary device between a computer and a laboratory-scaled model of a wind turbine. This type of wind turbine is used to educate students and engineers and to extract experimental wind power data. However, since commercial inverters don’t follow the main reference signal which is sent from the computer in order to operate the laboratory-scaled wind turbine, a controllable and variable inverter needed to be designed to receive that signal. The results are as follows. The voltage source inverter (VSI) and the current-controlled voltage source inverter (CC-VSI) were built on the VSI platform, and the current source inverter (CSI) was built on the CSI platform. Furthermore, the TMS320F2812’s analog digital converter (ADC) driver circuit and the output LC filter were also designed as auxiliary circuits. Five PWM switching programs were written; three switching algorithms for the VSI, and one algorithm each for the CC-VSI and the CSI. The output waveforms from the combination of hardware and software mentioned above were captured, and they follow the main reference signal very well. Although each of the inverters performed well, the VSI in combination with the Space Vector PWM switching algorithm produced the cleanest output voltage waveforms with the least amount of noise. The inverters built in this thesis project can be applied to the laboratory-scaled wind turbine, the maximum power tracking in solar panels, and equipment for analyzing digital signal processing. However, before using the inverters in those applications, much work remains to be done to solve the problems related to the signal distortion caused by the dead band time, harmonic signals caused by the fixed switching frequency, and the reliability issues caused by mounting on the bread board. In conclusion, although this thesis does not illustrate the entire process of or explain every requirement for building the three inverters, enough information about the topology of the inverters, the hardware design, and the PWM switching algorithms is provided in this thesis to enable one to remake all three of the three-phase inverters.Electrical and Computer Engineerin

A Demand Forecasting Framework With Stagewise, Piecewise, and Pairwise Selection Techniques

We propose an innovative electricity demand forecasting framework based on three model-selection techniques to maximize the forecasting accuracy. In the framework, we forecast the day-ahead electricity demand every 15 minutes based on temperature and cloud data, which are sampled from 35 weather stations. We develop three progressive techniques for selecting the model and data. First, using a stagewise forward technique, we select highly-correlated weather stations and group the best combination of selected stations. Second, using a piecewise series technique, we select the best performing forecasting machine every hour by comparing the forecasting accuracy of four forecasting machines. Third, we develop a pairwise mapping technique to combine two tandem forecasting models at the smaller sampling interval when the sampling intervals of weather and demand data differ. We verify that the framework based on three selection techniques results in higher forecasting accuracy using data from the 2018 RTE demand forecasting competition held in France

Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing

Total daily solar irradiation for the next day is forecasted through an ensemble of multiple machine learning algorithms using forecasted weather scenarios from numerical weather prediction (NWP) models. The weather scenarios were predicted at grid points whose longitudes and latitudes are integers, but the total daily solar irradiation was measured at non-integer grid points. Therefore, six interpolation functions are used to interpolate weather scenarios at non-integer grid points, and their performances are compared. Furthermore, when the total daily solar irradiation for the next day is forecasted, many data trimming techniques, such as outlier detection, input data clustering, input data pre-processing, and output data post-processing techniques, are developed and compared. Finally, various combinations of these ensemble techniques, different NWP scenarios, and machine learning algorithms are compared. The best model is to combine multiple forecasting machines through weighted averaging and to use all NWP scenarios

Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model

The deterministic methods generally used to solve DC optimal power flow (OPF) do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM)—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC) algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h

A Long-Term Evaluation on Transmission Line Expansion Planning with Multistage Stochastic Programming

The purpose of this paper is to apply multistage stochastic programming to the transmission line expansion planning problem, especially when uncertain demand scenarios exist. Since the problem of transmission line expansion planning requires an intensive computational load, dual decomposition is used to decompose the problem into smaller problems. Following this, progressive hedging and proximal bundle methods are used to restore the decomposed solutions to the original problems. Mixed-integer linear programming is involved in the problem to decide where new transmission lines should be constructed or reinforced. However, integer variables in multistage stochastic programming (MSSP) are intractable since integer variables are not restored. Therefore, the branch-and-bound algorithm is applied to multistage stochastic programming methods to force convergence of integer variables.In addition, this paper suggests combining progressive hedging and dual decomposition in stochastic integer programming by sharing penalty parameters. The simulation results tested on the IEEE 30-bus system verify that our combined model sped up the computation and achieved higher accuracy by achieving the minimised cost