Automatic Identification of Fault Types in the Distribution Network using Supervised Learning

Electrical faults in the distribution system can lead to interruptions in customer power supply resulting in penalties that are borne by the distribution system operator. Accurate fault classification is an important step in locating the fault to achieve faster network restoration times. This reduces the operational costs of the system operator...

State Estimation in Medium Voltage Distribution Networks

Transition from fossil fuels to sustainable sources of energy like wind and solar is the need of the hour. All over the globe, plans are in motion to achieve this goal. This implies addition of new elements to the grid in the form of Distributed Energy Resources (DERs). These affect the working of distribution grids and to ensure reliable as...

Unsupervised Learning to Locate Weak Spots in the Medium Voltage Grid

The Medium Voltage (MV) network in the Netherlands is almost entirely composed of underground cables. The failure statistics show that the interruptions in the medium voltage have a high contribution in the mean outage time per year per customer. The interruptions in the medium voltage grid are often caused by failure of circuit components...

Monte Carlo Sampling Techniques for the Efficient Estimation of Risk Metrics of a Stochastic Distribution Grid Power Demand Model

The Distribution System Operator (DSO) Alliander has the ambition to explore possibilities of improving its predictive demand modelling applications. This research aims to contribute to one of these, the Advanced Net DEcision Support (ANDES) model, which provides detailed predictions for long-term capacity planning. This thesis pursues two main...

Real Time Market Based Control of Flexible Distributed Energy Resources

Advancements in the field of Information and Communication Technologies (ICT) has enabled the possibility to utilize the flexibility offered by responsive assets in a better way by employing Demand Response (DR) schemes. This thesis analyzes the performance of one such DR scheme developed at TU Delft called Forecast mediated Market Based Control...

Optimising Grid Topology Reconfiguration using Reinforcement Learning

The de-carbonisation of the energy system, more commonly known as the 'Energy Transition' has a vital role to play in the pursuit of mitigating the climate emergency’s impact. There is a global trend of moving toward making power systems more future-proof and this largely affects the roles and activities of a Transmission System Operator (TSO)...

Quantifying the Contribution of distributed flexible loads to congestion management

The electrical network must have adequate capacity to avoid congestion for all but the highest load levels. The most common solution to manage this congestion is to gradually invest in new infrastructure. An alternative solution for congestion management is the use of flexible loads, or demand response. In order to determine the relative value...

Assessment of machine learning algorithms for the purpose of heat pump detection based on load profiles and temperature readings

The aim of this research thesis is to use machine learning models to distinguish owners of heat pumps from non-owners of heat pumps based on load profiles and temperature data only. As is the case with data mining project, its workflow can be divided into business understanding, data gathering, analysis, modeling and interpretation and...

Detecting a change in building electricity consumption patterns from electricity master meter data

The aim of this project was to develop an automated tool which can help monitoring agents to identify abnormal building behaviour by making use of the electricity master meter data. One of the most important requirements for this project was that a regression model is the starting point of the automated tool. Therefore, it was decided that, with...