Development of innovative robust stability enhancement algorithms for distribution systems containing distributed generators

This project was a step forward in improving the voltage profile of traditional low voltage distribution networks with high photovoltaic generation or high peak demand. As a practical and economical solution, the developed methods use a Dynamic Voltage Restorer or DVR, which is a series voltage compensator, for continuous and communication-less power quality enhancement. The placement of DVR in the network is optimised in order to minimise its power rating and cost. In addition, new approaches were developed for grid synchronisation and control of DVR which are integrated with the voltage quality improvement algorithm for stable operation.

Probabilistic load flow for distribution systems with uncertain PV generation

Large integration of solar Photo Voltaic (PV) in distribution network has resulted in over-voltage problems. Several control techniques are developed to address over-voltage problem using Deterministic Load Flow (DLF). However, intermittent characteristics of PV generation require Probabilistic Load Flow (PLF) to introduce variability in analysis that is ignored in DLF. The traditional PLF techniques are not suitable for distribution systems and suffer from several drawbacks such as computational burden (Monte Carlo, Conventional convolution), sensitive accuracy with the complexity of system (point estimation method), requirement of necessary linearization (multi-linear simulation) and convergence problem (Gram–Charlier expansion, Cornish Fisher expansion). In this research, Latin Hypercube Sampling with Cholesky Decomposition (LHS-CD) is used to quantify the over-voltage issues with and without the voltage control algorithm in the distribution network with active generation. LHS technique is verified with a test network and real system from an Australian distribution network service provider. Accuracy and computational burden of simulated results are also compared with Monte Carlo simulations.

Electrical conduction in plasma polymerized thin films of γ-terpinene

Plasma polymerized c-terpinene (pp2GT) thin films are fabricated using RF plasma polymerization. MIM structures are fabricated and using the capacitive structures dielectric properties of the material is studied. The dielectric constant values are found to be in good agreement with those determined from ellipsometric data. At a frequency of 100 kHz, the dielectric constant varies with RF deposition power, from 3.69 (10 W) to 3.24 (75 W). The current density–voltage (J2V) characteristics of pp–GT thin films are investigated as a function of RF deposition power at room temperature to determine the resistivity and DC conduction mechanism of the films. At higher applied voltage region, Schottky conduction is the dominant DC conduction mechanism. The capacitance and the loss tangent are found to be frequency dependent. The conductivity of the pp2GT thin films is found to decrease from 1.39 3 10212 S/cm (10 W) to 1.02 3 10213 S/cm (75 W) and attributed to the change in the chemical composition and structure of the polymer. The breakdown field for pp–GT thin films increases from 1.48 MV/cm (10 W) to 2 MV/cm (75 W). A single broad relaxation peak is observed indicating the contribution of multiple relaxations to the dielectric response for temperature dependent J2V. The distribution of these relaxation times is determined through regularization methods. VC 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42318.

A flexible tool for integrated planning of active distribution networks

This paper presents a flexible and integrated planning tool for active distribution network to maximise the benefits of having high level s of renewables, customer engagement, and new technology implementations. The tool has two main processing parts: “optimisation” and “forecast”. The “optimization” part is an automated and integrated planning framework to optimize the net present value (NPV) of investment strategy for electric distribution network augmentation over large areas and long planning horizons (e.g. 5 to 20 years) based on a modified particle swarm optimization (MPSO). The “forecast” is a flexible agent-based framework to produce load duration curves (LDCs) of load forecasts for different levels of customer engagement, energy storage controls, and electric vehicles (EVs). In addition, “forecast” connects the existing databases of utility to the proposed tool as well as outputs the load profiles and network plan in Google Earth. This integrated tool enables different divisions within a utility to analyze their programs and options in a single platform using comprehensive information.