Fuzzy Interference System in Energy Demand Prediction

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Superconducting Magnetic Energy Storage Unit for Damping Enhancement of a Wind Farm Generation System

A superconducting magnetic energy storage (SMES) system containsa high inducting coil and combines with power conversion system can act as a constant source of direct current. SMES unit connected to a power system is able to absorb and store both active and reactive power from this system and to inject these powers into the power system in the demand periods. These injected powers are controlled by changing both the duty cycle of the dc-dc chopper switches and its operation modes. This paper presents an efficient design based on an SMES unit controlled by the artificial neural network (ANN) to improve transient stability by regulating the dc link voltage and to damp the voltage and frequency fluctuations that are always associated with wind power generator. The authors propose interfacing the SMES between wind power farm and the power grid connected through the DC Link capacitor to rapidly stabilize the voltage and frequency fluctuations in the power system.The system behavior is tested with three different events for both voltage and frequency fluctuations of wind power generation with and without applying the SMES unit. The results show that both voltage and frequency stabilities are significantly increased when the SMES unit is applied in these three events

Stability Analysis of an Autonomous Microgrid Operation Based on Particle Swarm Optimization

This paper presents the stability analysis for an inverter based Distributed Generation (DG) unit in an autonomous microgrid operation. The small-signal model of the controlled Voltage Source Inverter (VSI) system is developed in order to investigate the dynamic stability for the given operating point and under the proposed power controller. This model includes all the details of the proposed controller, while no switching actions are considered. System oscillatory modes and the sensitivity to the control parameters are the main performance indices which are considered, particularly when the microgrid is islanded or under the load change condition. In this work, the proposed power controller is composed of an inner current control loop and an outer power control loop, both based on a synchronous reference frame and conventional PI regulators. These controllers also utilize the Particle Swarm Optimization (PSO) for real-time self-tuning in order to improve the quality of the power supply. The complete small-signal model is linearized and used to define the system state matrix which is employed for eigenvalue analysis. The results prove that the stability analysis is fairly accurate and the controller offers reliable system\u27s operation

Tuning Fuzzy Systems to Achieve Economic Dispatch for Microgrids

In this paper, a Tuning Fuzzy System (TFS) is used to improve the energy demand forecasting for a medium-size microgrid. As a case study, the energy demand of the Joondalup Campus of Edith Cowan University (ECU) in Western Australia is modelled. The developed model is required to perform economic dispatch for the ECU microgrid in islanding mode. To achieve an active economic dispatch demand prediction model, actual load readings are considered. A fuzzy tuning mechanism is added to the prediction model to enhance the prediction accuracy based on actual load changes. The demand prediction is modelled by a Fuzzy Subtractive Clustering Method (FSCM) based Adaptive Neuro Fuzzy Inference System (ANFIS). Three years of historical load data which includes timing information is used to develop and verify the prediction model. The TFS is developed from the knowledge of the error between the actual and predicted demand values to tune the prediction output. The results show that the TFS can successfully tune the prediction values and reduce the error in the subsequent prediction iterations. Simulation results show that the proposed prediction model can be used for performing economic dispatch in the microgrid

PSO algorithm for an optimal power controller in a microgrid

This paper presents the Particle Swarm Optimization (PSO) algorithm to improve the quality of the power supply in a microgrid. This algorithm is proposed for a real-time selftuning method that used in a power controller for an inverter based Distributed Generation (DG) unit. In such system, the voltage and frequency are the main control objectives, particularly when the microgrid is islanded or during load change. In this work, the PSO algorithm is implemented to find the optimal controller parameters to satisfy the control objectives. The results show high performance of the applied PSO algorithm of regulating the microgrid voltage and frequency

Robust placement and sizing of charging stations from a novel graph theoretic perspective

This paper proposes analytical approaches to extend the capacity of existing networks of electric vehicles (EVs) by placement of additional charging stations (CSs) as well as determining the sizes of existing and new CSs in order to handle future expansions of EVs. The EV flow at CSs is modeled by a graph where nodes are potential locations for CSs and edges are uncertain parameters representing the variable EV flow at CSs. The required extra CS locations are explored by transforming the CS placement problem into a controllability framework addressed by maximum matching principle (MMP). To find the sizes of each CS, the graph of CS network is partitioned featuring only one CS in each subgraph. The size of CS in each subgraph is then determined by transforming the problem into the problem of robust stability of a system with uncertain parameters where each parameter is associated with an edge of subgraph. The zero exclusion principle is then tested for the related Kharitonov rectangles and polygonal polynomials of closed loop system with selected feedback gain as CS capacity. The proposed analytical approach is tested on the existing Tesla CS Network of Sydney. The locations of extra required CSs as well as the sizes of existing and new CSs are determined to maintain the waiting times at all stations below the threshold level

A graph automorphic approach for placement and sizing of charging stations in EV network considering traffic

This paper proposes a novel graph-based approach with automorphic grouping for the modelling, synthesis, and analysis of electric vehicle (EV) networks with charging stations (CSs) that considers the impacts of traffic. The EV charge demands are modeled by a graph where nodes are positioned at potential locations for CSs, and edges represent traffic flow between the nodes. A synchronization protocol is assumed for the network where the system states correspond to the waiting time at each node. These models are then utilized for the placement and sizing of CSs in order to limit vehicle waiting times at all stations below a desirable threshold level. The main idea is to reformulate the CS placement and sizing problems in a control framework. Moreover, a strategy for the deployment of portable charging stations (PCSs) in selected areas is introduced to further improve the quality of solutions by reducing the overshooting of waiting times during peak traffic hours. Further, the inherent symmetry of the graph, described by graph automorphisms, are leveraged to investigate the number and positions of CSs. Detailed simulations are performed for the EV network of Perth Metropolitan in Western Australia to verify the effectiveness of the proposed approach

Image capture using integrated 3D SoftChip technology

Mobile multimedia communication has rapidly become a significant area of research and development. The processing requirements for the capture, conversion, compression, decompression, enhancement, display, etc. of high quality multimedia content places heavy demands even on current ULSI (ultra large scale integration) systems, particularly for mobile applications where area and power are primary considerations. The system presented is designed as a vertically integrated (3D) system comprising two distinct layers bonded together using indium bump technology. The top layer is a CMOS imaging array containing analog-to-digital converters, and a buffer memory. The bottom layer takes the form of a configurable array processor (CAP), a highly parallel array of soft programmable processors capable of carrying out complex processing tasks directly on data stored in the top plane. Until recently, the dominant format of data in imaging devices has been analog. The analog photocurrent or sampled voltage is transferred to the ADC via a column or a column/row bus. In the proposed system, an array of analog-to-digital converters is distributed, so that a one-bit cell is associated with one sensor. The analog-to-digital converters are algorithmic current-mode converters. Eight such cells are cascaded to form an 8-bit converter. Additionally, each photosensor is equipped with a current memory cell, and multiple conversions are performed with scaled values of the photocurrent for colour processing

Load demand forecasting: Model inputs selection

Developing a good demand forecasting model is the art of identifying the best modelling parameters. Improving the forecasting performance needs to study the input/output parameters of the system to identify the effective forecasting variables. In this paper, the energy demand of Joondalup Campus of Edith Cowan University (ECU) in Western Australia has been selected as a case study for the design and verification of a suitable forecasting model. Fuzzy Subtractive Clustering Method (FSCM) based Adaptive Neuro Fuzzy Inference System (ANFIS) is used as a proposed modelling network in this paper. Basically, three-input forecasting models have been developed based on 12-month models to perform ECU energy demand forecasting. The input/output parameters selection was made after analysing the historical demand pattern in ECU energy system. Generally, increasing the number inputs in model network may have wider training scope and better forecasting accuracy. However, the wrong choice of the additional input would deteriorate the forecasting accuracy. From analysing the historical operation of ECU energy system, four and five-input variables could be identified and modelling has been performed. The result show that four-input models were the best in the prediction performance among 12-month models of the annual demand predicion of ECU

POPULATION BIOLOGY OF MUSSELS (Aulacomya maoriana, Mytilus galloprovincialis and Perna canaliculus) FROM ROCKY INTERTIDAL SHORES IN WELLINGTON HARBOUR, NEW ZEALAND.

The land that grew the roots That grew me And the sea that fed the fish and birds That fed me And the wind that drove the world And knew me And blew over all-(Keri Hulme, “Stonefish”) ACKNOWLEDGEMENTS Firstly, I would like to thank my supervisor, Dr Jonathan Gardner, for his support and guidance during this research, and his professional and helpful attitude. I am also greatly indebted to Dr Shirley Pledger, whose patience, statistical expertise and passion for puzzles taught me a lot, and guided me through the statistical wonderland. Thanks go to Dr Wendy Nelson from NIWA, Wellington, for finding a spare minute to help me with algae identification from my digital pictures. I also thank Dr Jeremy Helson, a forme