Electrical and Electronic Engineering - Theses
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ItemNovel Defenses Against Data Poisoning in Adversarial Machine Learning( 2019)Machine learning models are increasingly being used for automated decision making in a wide range of domains such as security, finance, and communications. Machine learning algorithms are built upon the assumption that the training data and test data have the same underlying distribution. This assumption fails when (i) data naturally evolves, causing the test data distribution to diverge from the training data distribution, and (ii) malicious adversaries distort the training data (i.e., poisoning attacks), which is the focus of this thesis. Even though machine learning algorithms are used widely, there is a growing body of literature suggesting that their prediction performance degrades significantly in the presence of maliciously poisoned training data. The performance degradation can mainly be attributed to the fact that most machine learning algorithms are designed to withstand stochastic noise in data, but not malicious distortions. Through malicious distortions, adversaries aim to force the learner to learn a model that differs from the model it would have learned had the training data been pristine. With the models being compromised, any systems that rely on the models for automated decision making would be compromised as well. This thesis presents novel defences for machine learning algorithms to avert the effects of poisoning attacks. We investigate the impact of sophisticated poisoning attacks on machine learning algorithms such as Support Vector Machines (SVMs), one-class Support Vector Machines (OCSVMs) and regression models, and introduce new defences that can be incorporated into these models to achieve more secure decision making. Specifically, two novel approaches are presented to address the problem of learning under adversarial conditions as follows. The first approach is based on data projections, which compress the data, and we examine the effect of the projections on adversarial perturbations. By projecting the training data to lower-dimensional spaces in selective directions, we aim to minimize the impact of adversarial feature perturbations on the training model. The second approach uses Local Intrinsic Dimensionality (LID), a metric that characterizes the dimension of the local subspace in which data samples lie, to distinguish data samples that may have been perturbed (feature perturbation or label flips). This knowledge is then incorporated into existing learning algorithms in the form of sample weights to reduce the impact of poisoned samples. In summary, this thesis makes a major contribution to research on adversarial machine learning by (i) investigating the effects of sophisticated attacks on existing machine learning models and (ii) developing novel defences that increase the attack resistance of existing models. All presented work is supported by theoretical analysis, empirical results, and is based on publications.
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ItemImpact of Rooftop Solar on Distribution Network Planning( 2019)Electricity networks have been undergoing significant transformation recently, especially in terms of embedded generation. There has been a lot of focus on demand fluctuations from solar and wind farms that are being connected onto high voltage (HV) grids in energy markets. But the distribution low voltage (LV) grid may prove the most challenging for the network owners and market operators. This is because rooftop solar, whether installed in commercial or residential areas, is leading to high demand fluctuations within the last mile. Customer-installed solar is also causing voltages to rise, but it is the Distribution Network Operator (DNO) on which the responsibility of voltage regulation falls. There is hence greater importance for the DNO to have full visibility of the LV feeder voltages at all times, accurately analysing proposed connections, and meeting the regulators’ and government expectations of enabling solar penetration. Voltage monitoring and regulating infrastructure at the LV level, though, is expensive to implement and hence scarce due to its huge scale. Utilities hence employ empirical or statistical techniques to calculate voltage drop and voltage rise. Conservative allowances for demand diversity and unbalance can lead to erroneous results and can form the basis of considerable utility capital expenditure programs. Utility expenditure in turn usually leads to an increase in customer bills over time. A small number of utilities in the world have access to voltage data from smart metering infrastructures, such as in Victoria, Australia, but ownership of data is becoming an open question. Data availability also presents a different problem to them, as these meters are leading to an extraordinary amount of near real-time data, which they are failing to fully embrace. They see smart-technology driven initiatives as a form of disruption and are slow or unwilling to adapt to the changing nature of the grid. This dissertation details the use of data analytics for forecasting future voltages on the network. Standard machine learning techniques are used to create a non-linear regression model fit to train parameters that reflect the operational status of the feeder. These parameters reflect load diversity and unbalance as well as generator diversity and unbalance. The trained model consequently accurately predicts voltages on the feeder with additional connections. A load-flow simulation of a real-world network is carried out. Training and testing are performed on data from different halves of the year. Predicted voltages are compared to simulation results to confirm the high accuracy, even though consumption patterns and solar irradiation patterns change due to different seasons in the test data. Hence, by leveraging interval metering data, it is shown how standard machine learning methods can be used to develop forecasting capabilities. The methodology developed in this thesis can used as a planning tool to quickly and accurately evaluate future rate of recurrence of voltage violations; and predict the voltage headroom available on the LV feeder. This is a significant outcome as predictability of LV feeder voltages is a concern for the utilities, consumers as well as regulating bodies. The presented method will enable more loads and PVs onto the network without the need of new assets such as distribution transformers or LV feeders, that may be left underutilised. It will also help resolve certain quality of supply issues such as voltage drop complaints; and help better prioritise and technically analyse constrained areas of the network. It is clear that high-quality, high-volume data analysis will play a key role in resolving the needs of the electricity industry. This thesis serves as an interface between network planning engineers and data scientists who will solve the emerging energy constraints, play a part in minimising customer energy prices and assist in the transition to decentralised clean energy sources.