Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results

This paper focuses on current control in a permanentmagnet synchronous motor (PMSM). The paper has two main objectives: The first objective is to develop a neural-network (NN) vector controller to overcome the decoupling inaccuracy problem associated with conventional PI-based vector-control methods. The NN is developed using the full dynamic equation of a PMSM, and trained to implement optimal control based on approximate dynamic programming. The second objective is to evaluate the robust and adaptive performance of the NN controller against that of the conventional standard vector controller under motor parameter variation and dynamic control conditions by (a) simulating the behavior of a PMSM typically used in realistic electric vehicle applications and (b) building an experimental system for hardware validation as well as combined hardware and simulation evaluation. The results demonstrate that the NN controller outperforms conventional vector controllers in both simulation and hardware implementation

Integrating Hybrid Off-grid Systems with Battery Storage: Key Performance Indicators

A clear opportunity exists for the integration of Battery Energy Storage Systems (BESS) in hybrid off-grid applications, i.e., isolated grids with renewable sources (e.g. PV, wind) and small-scale diesel generators. In these applications, renewable sources have the potential to reduce petroleum derivatives consumption (diesel, lubricants, etc.) and reduce Greenhouse Gases emissions. Therefore, in recent years the changes in the economics of renewables and, particularly, PV sources, have led to their integration with diesel generators in order to reduce the Operational Expenditure (OPEX) of off-grid systems. BESS present the capability of maximizing the integration of renewable energy and, consequently, further offset the use of diesel-fired generating units. The purpose of this work is twofold: First, the objective is to identify the Key Performance Indicators (KPI) for assessing the integration of Hybrid Off-grid Systems with BESS. Second, these KPI, reflecting the potential impacts of battery Storage within an Hybrid Off-grid system, will enable the assessment of the business case of the BESS integration

Optimal energy controllers of energy storage systems based on load forecasting for RTG cranes network

Given the increased international trading in ports around the world, there are significant challenges facing ports such as rising energy consumption and greenhouse emissions. The electrification of Rubber Tyred Gantry (RTG) cranes is one approach used to reduce gas emissions and fuel costs at port, but has also increased the electrical demand across the electrical distribution network. This will force port operators to reinforce the low voltage network to meet this increased demand and remain within the operating constraints. An energy storage system is one potential solution to increase the energy efficiency of the low voltage distribution networks whilst avoiding expensive reinforcement of the power system. This thesis aims to highlight and address the peak demand problem in the network of electrified RTG cranes and attempts to reduce peak demand and electricity costs by optimality controlling the energy storage system by utilising load forecasts. Since there is currently lack of understanding of the volatile demand behaviour, the research begins by investigating the unique characteristics of the electrical demand of the RTG crane. This understanding is a vital tool to develop an accurate forecast model and maximise the benefits of using an energy storage system through a control system. Several short-term load forecast models have been developed based on the ARIMAX and ANN models to predict accurate day-ahead electrical R TG crane demand. The forecast results show that the highly volatile demand behaviour creates a substantial prediction challenge compared to normal residential low voltage network demand. This thesis then presents the significance of forecasting the crane demand to improve the energy performance of an electrical distribution network with an ESS by employing several optimal controllers. The novel optimal control algorithms considered for the network of RTG cranes are split into: a Model Predictive Controller (MPC) with rolling forecast system and a Stochastic Model Predictive Controller (SMPC) based on a stochastic prediction demand model. The proposed MPC and SMPC control models are compared to an optimal controller based on a fixed load forecast profile and a common and standard set-point controller. Results show that the optimal controllers based on a load forecast have improved the storage device performance for the peak reduction and cost savings compared to the traditional control algorithm. Further improvements are then presented for the receding horizon controllers, MPC and SMPC, which better treat the volatility of the crane demand and the uncertainty in the forecasts. Furthermore, an economic analysis of the results for different ESS location scenarios is presented to assess their viability