Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller

This paper presents a new approach to control the operation of the unified power-flow controller (UPFC) based on the adaptive neurofuzzy inference controller (ANFIC) concept. The training data for the controller are extracted from an analytical model of the transmission system incorporating a UPFC. The operating points' space is dynamically partitioned into two regions: 1) an inner region where the desired operating point can be achieved without violating any of the UPFC constraints and 2) an outer region where it is necessary to operate the UPFC beyond its limits. The controller is designed to achieve the most appropriate operating point based on the real power priority. In this study, the authors investigated and analyzed the effect of the system short-circuit level on the UPFC operating feasible region which defines the limitation of its parameters. In order to illustrate the effectiveness of the control algorithm, simulation and experimental studies have been conducted using the MATLAB/SIMULINK and dSPACE DS1103 data-acquisition board. The obtained results show a clear agreement between simulation and experimental results which verify the effective performance of the ANFIC controller

Investigation of advanced control for the unified power flow controller (UPFC)

The Unified Power Flow Controller (UPFC) is the versatile FACTS controller that can control up to three transmission system parameters individually or simultaneously in appropriate combinations. The work presented in this thesis is concentrated on the modelling and control of the UPFC. The overall aim is to provide effective tools for optimising the impact of the UPFC in the reinforcement of a transmission system. Existing modelling techniques for the UPFC together with the associated control strategies have been systematically reviewed. An exact power injection model is proposed which is based on the polar representation of the UPFC parameters and includes the reactive power capability of the shunt inverter. In addition, a steady-state model based on an ideal controlled voltage source has been developed using MATLAB/SIMULINK which provides a useful tool to analyse and develop the UPFC control system. The UPFC internal limits have been identified and accordingly, the feasible operating area of a transmission system incorporating a UPFC has been determined based on the UPFC maximum limits. The influence of both the series and shunt inverters on this controlled area has been analysed. The impact of a change in the system short circuit level on the UPFC operation and the size of the feasible area has also been investigated. Three modern controllers have been designed and tested for controlling the UPFC in a power flow mode for the series part and a voltage control mode for the shunt part. These controllers are: a fuzzy knowledge based controller, an artificial neural network based controller and a neuro-fuzzy based controller. For the former, the fuzzy rules are deduced from the relationship between the controlled power system parameters and the UPFC control variables. The second is a simple RBFNN controller which is constructed from a single neuron and trained on-line by a gradient descent algorithm. The third controller is designed using the adaptive capabilities of neural networks to estimate and tune the fuzzy rules. Computer simulation and experimental implementation of a UPFC using DS 1103 data acquisition board have been used to verify the proposed control strategies. In the experimental lab model, two 6-pulse inverters implementing the SPWM technique have been used to realise the UPFC system

Quantification of efficiency improvements from integration of battery energy storage systems and renewable energy sources into domestic distribution networks

Due to the increasing use of renewable, non-controllable energy generation systems energy storage systems (ESS) are seen as a necessary part of future power delivery systems. ESS have gained research interest and practical implementation over the past decade and this is expected to continue into the future. This is due to the economic and operational benefits for both network operators and customers, battery energy storage system (BESS) is used as the main focus of this research paper. This paper presents an analytical study of the benefits of deploying distributed BESS in an electrical distribution network (DN). The work explores the optimum location of installing BESS and its impact on the DN performance and possible future investment. This study provides a comparison between bulk energy storage installed at three different locations; medium voltage (MV) side and low voltage (LV) side of the distribution transformer (DT) and distributed energy storage at customers’ feeders. The performance of a typical UK DN is examined under different penetration levels of wind energy generation units and BESS. The results show that the minimum storage size is obtained when BESS is installed next to the DT. However, the power loss is reduced to its minimum when BESS and wind energy are both distributed at load busbars. The study demonstrates that BESS installation has improved the loss of life factor of the distribution transformer