Doubly Fed Induction Generators : Overview and Intelligent Control Strategies for Wind Energy Conversion Systems

Adjustable speed induction generators, especially the Doubly-Fed Induction Generators (DFIG) are becoming increasingly popular due to its various advantages over fixed speed generator systems. A DFIG in a wind turbine has ability to generate maximum power with varying rotational speed, ability to control active and reactive by integration of electronic power converters such as the back-to-back converter, low rotor power rating resulting in low cost converter components, etc, DFIG have become very popular in large wind power conversion systems. This chapter presents an extensive literature survey over the past 25 years on the different aspects of DFIG. Application of H8 Controller for enhanced DFIG-WT performance in terms of robust stability and reference tracking to reduce mechanical stress and vibrations is also demonstrated in the chapter.

Application of TS-Fuzzy Controller for Active Power and DC Capacitor Voltage Control in DFIG-Based Wind Energy Conversion Systems

This chapter focuses on the implementation of the TS (Tagaki-Sugino) fuzzy controller for the Doubly Fed Induction Generator (DFIG) based wind generator. The conventional PI control loops for mantaining desired active power and DC capacitor voltage is compared with the TS fuzzy controllers. DFIG system is represented by a third-order model where electromagnetic transients of the stator are neglected. The effectiveness of the TS-fuzzy controller on the rotor speed oscillations and the DC capacitor voltage variations of the DFIG damping controller on converter ratings is also investigated. The results from the time domain simulations are presented to elucidate the effectiveness of the TS-fuzzy controller over the conventional PI controller in the DFIG system. The proposed TS-fuzzy con-troller can improve the fault ride through capability of DFIG compared to the conventional PI controller.

Application of anomaly technique in wind turbine bearing fault detection

Bearing faults are the most common cause of wind turbine failures. Unavailability and maintenance cost of wind turbines are becoming critically important, with their fast growing in electric networks. Early fault detection can reduce outage time and costs. This paper proposes Anomaly Detection (AD) machine learning algorithms for fault diagnosis of wind turbine bearings. The application of this method on a real data set was conducted and is presented in this paper. For validation and comparison purposes, a set of baseline results are produced using the popular one-class SVM methods to examine the ability of the proposed technique in detecting incipient faults.

A new constraint handling method for wind farm layout optimization with lands owned by different owners

For wind farm optimizations with lands belonging to different owners, the traditional penalty method is highly dependent on the type of wind farm land division. The application of the traditional method can be cumbersome if the divisions are complex. To overcome this disadvantage, a new method is proposed in this paper for the first time. Unlike the penalty method which requires the addition of penalizing term when evaluating the fitness function, it is achieved through repairing the infeasible solutions before fitness evaluation. To assess the effectiveness of the proposed method on the optimization of wind farm, the optimizing results of different methods are compared for three different types of wind farm division. Different wind scenarios are also incorporated during optimization which includes (i) constant wind speed and wind direction; (ii) various wind speed and wind direction, and; (iii) the more realisticWeibull distribution. Results show that the performance of the new method varies for different land plots in the tested cases. Nevertheless, it is found that optimum or at least close to optimum results can be obtained with sequential land plot study using the new method for all cases. It is concluded that satisfactory results can be achieved using the proposed method. In addition, it has the advantage of flexibility in managing the wind farm design, which not only frees users to define the penalty parameter but without limitations on the wind farm division.

Fault detection of wind turbine drivetrain utilizing power-speed characteristics

Wind energy, being the fastest growing renewable energy source in the present world, requires a large number of wind turbines to transform wind energy into electricity. One factor driving the cost of this energy is the reliable operation of these turbines. Therefore, it is a growing requirement within the wind farm community, to monitor the operation of the wind turbines on a continuous basis so that a possible fault can be detected ahead of time. As the wind turbine operates in an environment of constantly changing wind speed, it is a challenging task to design a fault detection technique which can accommodate the stochastic operational behavior of the turbines. Addressing this issue, this paper proposes a novel fault detection criterion which is robust against operational uncertainty, as well as having the ability to quantify severity level specifically of the drivetrain abnormality within an operating wind turbine. A benchmark model of wind turbine has been utilized to simulate drivetrain fault condition and effectiveness of the proposed technique has been tested accordingly. From the simulation result it can be concluded that the proposed criterion exhibits consistent performance for drivetrain faults for varying wind speed and has linear relationship with the fault severity level.

Fuzzy-logic based frequency controller for wind farms augmented with energy storage systems

Displacement of conventional synchronous generators by non-inertial units such as wind or solar generators will result in reduced-system inertia affecting under-frequency response. Frequency control is important to avoid equipment damage, load shedding, and possible blackouts. Wind generators along with energy storage systems can be used to improve the frequency response of low-inertia power system. This paper proposes a fuzzy-logic based frequency controller (FFC) for wind farms augmented with energy storage systems (wind-storage system) to improve the primary frequency response in future low-inertia hybrid power system. The proposed controller provides bidirectional real power injection using system frequency deviations and rate of change of frequency (RoCoF). Moreover, FFC ensures optimal use of energy from wind farms and storage units by eliminating the inflexible de-loading of wind energy and minimizing the required storage capacity. The efficacy of the proposed FFC is verified on the low-inertia hybrid power system.

Fast unscented transformation-based transient stability margin estimation incorporating uncertainty of wind generation

Intermittent generation from wind farms leads to fluctuating power system operating conditions pushing the stability margin to its limits. The traditional way of determining the worst case generation dispatch for a system with several semi-scheduled wind generators yields a conservative solution. This paper proposes a fast estimation of the transient stability margin (TSM) incorporating the uncertainty of wind generation. First, the Kalman filter (KF) is used to provide linear estimation of system angle and then unscented transformation (UT) is used to estimate the distribution of the TSM. The proposed method is compared with the traditional Monte Carlo (MC) method and the effectiveness of the proposed approach is verified using Single Machine Infinite Bus (SMIB) and IEEE 14 generator Australian dynamic system. This method will aid grid operators to perform fast online calculations to estimate TSM distribution of a power system with high levels of intermittent wind generation.

Pull-through failure tests of thin steel roof battens under wind uplift load

Thin profiled steel roof sheeting and battens are increasingly used in the construction of roofing systems of residential, commercial, industrial and farm buildings in Australia. The critical load combination of external wind suction and internal wind pressures that occur during high wind events such as thunderstorms and tropical cylcones often dislocate the roofing systems partially or even completely due to premature roof connection failures. Past wind damage investigations have shown that roof sheeting failures occured at their screw connections to battens. In most of these cases, the screw fastener head pulled through the thin roof sheeting whilst the screw fasteners also pulled out from the battens. Research studis undertaken on the roof sheeting to batten connection failures have improved this situation. However, the batten to rafter or truss connections have not been investigated adequately. Failure of these connections can cause the failure of the entire roof structure as observed during the recent high wind events. Therefore a detailed experimental study consisting of both small scale and full scale tests has been undertaken to investigate the steel roof batten pull-through failures in relation to many critical parameters such as steel batten geometry, thickness and grade, screw fastener head sizes and screw tightening. This paper presents the details of this experimental study and the pull-through failure load results obtained from them. Finally it discusses the development of suitable design rules that can be used to determine the pull-through connection capacities of thin steel roof battens under wind uplift loads.

Numerical modeling of thin steel roof battens under wind uplift loads

Extreme wind events such as tropical cyclones, tornadoes and storms are more likely to impact the Australian coastal regions due to possible climate changes. Such events can be extremely destructive to building structures, in particular, low-rise buildings with lightweight roofing systems that are commonly made of thin steel roofing sheets and battens. Large wind uplift loads that act on the roofs during high wind events often cause premature roof connection failures. Recent wind damage investigations have shown that roof failures have mostly occurred at the batten to rafter or truss screw connections. In most of these cases, the screw fastener heads pulled through the bottom flanges of thin steel roof battens. This roof connection failure is very critical as both roofing sheets and battens will be lost during the high wind events. Hence, a research study was conducted to investigate this critical pull-through failure using both experimental and numerical methods. This paper presents the details of numerical modeling and the results.

Different approaches of applying single-objective binary genetic algorithm on the wind farm design

This paper discusses three different ways of applying the single-objective binary genetic algorithm into designing the wind farm. The introduction of different applications is through altering the binary encoding methods in GA codes. The first encoding method is the traditional one with fixed wind turbine positions. The second involves varying the initial positions from results of the first method, and it is achieved by using binary digits to represent the coordination of wind turbine on X or Y axis. The third is the mixing of the first encoding method with another one, which is by adding four more binary digits to represent one of the unavailable plots. The goal of this paper is to demonstrate how the single-objective binary algorithm can be applied and how the wind turbines are distributed under various conditions with best fitness. The main emphasis of discussion is focused on the scenario of wind direction varying from 0° to 45°. Results show that choosing the appropriate position of wind turbines is more significant than choosing the wind turbine numbers, considering that the former has a bigger influence on the whole farm fitness than the latter. And the farm has best performance of fitness values, farm efficiency, and total power with the direction between 20°to 30°.

Optimum position of steel outrigger system for high rise composite buildings subjected to wind loads

The responses of composite buildings under wind loads clearly become more critical as the building becomes taller, less stiff and more lightweight. When the composite building increases in height, the stiffness of the structure becomes more important factor and introduction to belt truss and outrigger system is often used to provide sufficient lateral stiffness to the structure. Most of the research works to date is limited to reinforced concrete building with outrigger system of concrete structure, simple building plan layout, single height of a building, one direction wind and single level of outrigger arrangement. There is a scarcity in research works about the effective position of outrigger level on composite buildings under lateral wind loadings when the building plan layout, height and outrigger arrangement are varied. The aim of this paper is to determine the optimum location of steel belt and outrigger systems by using different arrangement of single and double level outrigger for different size, shape and height of composite building. In this study a comprehensive finite element modelling of composite building prototypes is carried out, with three different layouts (Rectangular, Octagonal and L shaped) and for three different storey (28, 42 and 57-storey). Models are analysed for dynamic cyclonic wind loads with various combination of steel belt and outrigger bracings. It is concluded that the effectiveness of the single and double level steel belt and outrigger bracing are varied based on their positions for different size, shape and height of composite building.

Towards the development of a low cost airborne sensing system to monitor dust particles after blasting at open-pit mine sites

Blasting is an integral part of large-scale open cut mining that often occurs in close proximity to population centers and often results in the emission of particulate material and gases potentially hazardous to health. Current air quality monitoring methods rely on limited numbers of fixed sampling locations to validate a complex fluid environment and collect sufficient data to confirm model effectiveness. This paper describes the development of a methodology to address the need of a more precise approach that is capable of characterizing blasting plumes in near-real time. The integration of the system required the modification and integration of an opto-electrical dust sensor, SHARP GP2Y10, into a small fixed-wing and multi-rotor copter, resulting in the collection of data streamed during flight. The paper also describes the calibration of the optical sensor with an industry grade dust-monitoring device, Dusttrak 8520, demonstrating a high correlation between them, with correlation coefficients (R2) greater than 0.9. The laboratory and field tests demonstrate the feasibility of coupling the sensor with the UAVs. However, further work must be done in the areas of sensor selection and calibration as well as flight planning.

Gone with the wind: copyright law and fair use

The copyright defence of fair use has been tested in a recent United States case involving the classic Gone with the wind. [Suntrust Bank, as Trustee of the Stephens Mitchell trusts v Houghton Mifflin Company (2001) 136 F. Supp. 2d 1357; and Suntrust Bank, as Trustee of the Stephens Mitchell trusts v Houghton Mifflin Company (2001) US Appeal Lexis 21690.]

Polybrominated diphenyl ethers (PBDEs) in dust from primary schools in South East Queensland, Australia

PBDE concentrations are higher in children compared to adults with exposure suggested to include dust ingestion. Besides the home environment, children spend a great deal of time in school classrooms which may be a source of exposure. As part of the “Ultrafine Particles from Traffic Emissions and Children's Health (UPTECH)” project, dust samples (n=28) were obtained in 2011/12 from 10 Brisbane, Australia metropolitan schools and analysed using GC and LC–MS for polybrominated diphenyl ethers (PBDEs) -17, -28, -47, -49, -66, -85, -99, -100, -154, -183, and -209. Σ11PBDEs ranged from 11–2163 ng/g dust; with a mean and median of 600 and 469 ng/g dust, respectively. BDE-209 (range n.d. −2034 ng/g dust; mean (median) 402 (217) ng/g dust) was the dominant congener in most classrooms. Frequencies of detection were 96%, 96%, 39% and 93% for BDE-47, -99, -100 and -209, respectively. No seasonal variations were apparent and from each of the two schools where XRF measurements were carried out, only two classroom items had detectable bromine. PBDE intake for 8–11 year olds can be estimated at 0.094 ng/day BDE-47; 0.187 ng/day BDE-99 and 0.522 ng/day BDE-209 as a result of ingestion of classroom dust, based on mean PBDE concentrations. The 97.5% percentile intake is estimated to be 0.62, 1.03 and 2.14 ng/day for BDEs-47, -99 and -209, respectively. These PBDE concentrations in dust from classrooms, which are higher than in Australian homes, may explain some of the higher body burden of PBDEs in children compared to adults when taking into consideration age-dependant behaviours which increase dust ingestion.

Speed-based diagnostics of aerodynamic and mass imbalance in large wind turbines

Any kind of imbalance in the operation of a wind turbine has adverse effect on the downstream torsional components as well as tower structure. It is crucial to detect imbalance at its very inception. The identification of the type of imbalance is also required so that appropriate measures of fault accommodation can be performed in the control system. In particular, it is important to distinguish between mass and aerodynamic imbalance. While the former is gradually caused by a structural anomaly (e.g. ice deposition, moisture accumulation inside blade), the latter is generally associated to a fault in the pitch control system. This paper proposes a technique for the detection and identification of imbalance fault in large scale wind turbines. Unlike most other existing method it requires only the rotor speed signal which is readily available in existing turbines. Signature frequencies have been proposed in this work to identify imbalance type based on their physical phenomenology. The performance of this technique has been evaluated by simulations using an existing benchmark model. The effectiveness of the proposed method has been confirmed by the simulation results.