Modeling toolkit for comparing AC vs. DC electrical distribution efficiency in buildings, A

2021 Summer.Includes bibliographical references.An increasing proportion of electrical devices in residential and commercial buildings operate from direct current (DC) power sources. In addition, distributed power generation systems such as solar photovoltaic (PV) and energy storage natively produce DC power. However, traditional power distribution is based on an alternating current (AC) model. Performing the necessary conversions between AC and DC power to make DC devices compatible with AC distribution results in energy losses. For these reasons, DC distribution may offer energy efficiency advantages in comparison to AC distribution. However, reasonably fast computation and comparison of electrical efficiencies of AC-only, DC-only, and hybrid AC/DC distributions systems is challenging because DC devices are typically (nonlinear) power-electronic converters that produce harmonic content. While detailed time-domain modeling can be used to simulate these harmonics, it is not computationally efficient or practical for many building designers. To address this need, this research describes a toolkit for computation of harmonic spectra and energy efficiency in mixed AC and DC electrical distribution systems, using a Harmonic Power Flow (HPF) methodology. The toolkit includes a library of two-port linear and nonlinear device models which can be used to construct and simulate an electrical distribution system. This dissertation includes a description of the mathematical theory and framework underlying the toolkit, development and fitting of linear and nonlinear device models, software implementation in Modelica, verification of the toolkit with laboratory measurements, and discussion of ongoing and future work to employ the toolkit to a variety of building designs

Object-oriented shipboard electric power system library

The objective of this thesis is to explore the powerful capabilities of using an object-oriented modeling language to model and simulate an all electric Naval Shipboard Power System. Modelica has been used to model and simulate the shipboard power system which acts as an alternative simulation tool. The shipboard system is developed using the concept of packages. Different components like the buck converter, inverter, and AC machines have been modeled as a part of the library to develop the power system. The shipboard system has been simulated as two decoupled systems, the AC and DC systems. This research further focuses on developing a networked protection system to detect and clear faults and protect the shipboard power system from complete breakdown. A discrete supervisory controller has been designed using Petri nets as part of the protection system to control the converters and clear faults. A communication network has also been modeled for communication. Two different case studies, the open circuit test, and short circuit test were performed to test the effectiveness of the protection system and the simulation results are presented. This thesis also gives an overview of different properties of Modelica along with its advantages over other simulation tools, a detailed survey of different types of object-oriented simulation tools available, a comparison of different power electronics simulation tools, and some of the previous work done in Modelica

Electromagnetic transient simulation of large power networks with Modelica

This paper presents the simulation of electromagnetic transients (EMTs) with Modelica. The advantages and disadvantages are discussed. Simulation performance and accuracy are analyzed through the IEEE 118-bus benchmark which includes EMT-detailed models with nonlinearities. The domain-specific simulator EMTP is used for validations and comparisons

Power System Stability Analysis using Neural Network

This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio