Near-field enhancement for infrared sensor applications

A detailed investigation on planar two dimensional metallodielectric dipole arrays with enhanced near-fields for sensing applications was carried out. Two approaches for enhancing the near-fields and increasing the quality factor were studied. The reactive power stored in the vicinity of the array at resonance increases rapidly with increasing periodicity. Higher quality factors are produced as a result. The excitation of the odd mode in the presence of a perturbation gives rise to a sharp resonance with near-field enhanced by at least an order of magnitude compared to unperturbed arrays. The trade-off between near-field enhancement and thermal losses was also studied, and the effect of supporting dielectric layers on thermal losses and quality factors were examined. Secondary transmissions due to the dielectric alone were found to enhance and reduce cyclically the quality factor as a function of the thickness of the dielectric material. The performance of a perturbed frequency selective surface in sensing nearby materials was investigated. Finally, unperturbed and perturbed arrays working at infrared frequencies were demonstrated experimentally. (C) 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.3604785]

Large-Scale Wind Generator Cascaded Tripping

Large scale wind farms are subject to tripping, as a consequence of turbine failure, over-sensitive protection, turbines not equipped with low-voltage ride through (LVRT), and reactive power compensation device defects which can lead to voltage rises. This paper considers pertinent issues which render tripping based on a study of LVRT and wind farm protection, with methods to avoid large scale wind generator tripping proposed. The results of LVRT field tests in Jiuquan, China in December 2012 show that the proposed approaches are effective. The paper also presents work which proposes an early warning system to forecast the risk of wind power tripping.

Impact of Heat Pump Load on Distribution Networks

Heat pumps can provide domestic heating at a cost that is competitive with oil heating in particular. If the electricity supply contains a significant amount of renewable generation, a move from fossil fuel heating to heat pumps can reduce greenhouse gas emissions. The inherent thermal storage of heat pump installations can also provide the electricity supplier with valuable flexibility. The increase in heat pump installations in the UK and Europe in the last few years poses a challenge for low-voltage networks, due to the use of induction motors to drive the pump compressors. The induction motor load tends to depress voltage, especially on starting. The paper includes experimental results, dynamic load modelling, comparison of experimental results and simulation results for various levels of heat pump deployment. The simulations are based on a generic test network designed to capture the main characteristics of UK distribution system practice. The simulations employ DIgSlILENT to facilitate dynamic simulations that focus on starting current, voltage variations, active power, reactive power and switching transients.

Distribution Network Voltage Support Using Sensitivity-based Dispatch of Distributed Generation

This paper is concerned with the voltage and reactive power issues surrounding the connection of Distributed Generation (DG) on the low-voltage (LV) distribution network. The presented system-wide voltage control algorithm consists of three stages. Firstly available reactive power reserves are utilized. Then, if required, DG active power output is curtailed. Finally, curtailment of non-critical site demand is considered. The control methodology is tested on a variant of the 13-bus IEEE Node Radial Distribution Test Feeder. The presented control algorithm demonstrated that the distribution system operator (DSO) can maintain voltage levels within a desired statutory range by dispatching reactive power from DG or network devices. The practical application of the control strategy is discussed.

Python PSS/E Simulation to Test Efficacy of Proposed PMU Based WAMS and Potential WAMPAC Applications

A PSS/E 32 model of a real section of the Northern Ireland electrical grid was dynamically controlled with Python 2.5. In this manner data from a proposed wide area monitoring system was simulated. The area is of interest as it is a weakly coupled distribution grid with significant distributed generation. The data was used to create an optimization and protection metric that reflected reactive power flow, voltage profile, thermal overload and voltage excursions. Step changes in the metric were introduced upon the operation of special protection systems and voltage excursions. A wide variety of grid conditions were simulated while tap changer positions and switched capacitor banks were iterated through; with the most desirable state returning the lowest optimization and protection metric. The optimized metric was compared against the metric generated from the standard system state returned by PSS/E. Various grid scenarios were explored involving an intact network and compromised networks (line loss) under summer maximum, summer minimum and winter maximum conditions. In each instance the output from the installed distributed generation is varied between 0 MW and 80 MW (120% of installed capacity). It is shown that in grid models the triggering of special protection systems is delayed by between 1 MW and 6 MW (1.5% to 9% of capacity), with 3.5 MW being the average. The optimization and protection metric gives a quantitative value for system health and demonstrates the potential efficacy of wide area monitoring for protection and control.

Proposed wavelet-neurofuzzy combined system for power qualityviolations detection and diagnosis

A system for the identification of power quality violations is proposed. It is a two-stage system that employs the potentials of the wavelet transform and the adaptive neurofuzzy networks. For the first stage, the wavelet multiresolution signal analysis is exploited to denoise and then decompose the monitored signals of the power quality events to extract its detailed information. A new optimal feature-vector is suggested and adopted in learning the neurofuzzy classifier. Thus, the amount of needed training data is extensively reduced. A modified organisation map of the neurofuzzy classifier has significantly improved the diagnosis efficiency. Simulation results confirm the aptness and the capability of the proposed system in power quality violations detection and automatic diagnosis

Idealised operation of zero-voltage-switching series-L/parallel-tuned Class-E power amplifier

An analysis of a modified series-L/parallel-tuned Class-E power amplifier is presented, which includes the effects that a shunt capacitance placed across the switching device will have on Class-E behaviour. In the original series L/parallel-tuned topology in which the output transistor capacitance is not inherently included in the circuit, zero-current switching (ZCS) and zero-current derivative switching (ZCDS) conditions should be applied to obtain optimum Class-E operation. On the other hand, when the output transistor capacitance is incorporated in the circuit, i.e. in the modified series-L/parallel-tuned topology, the ZCS and ZCDS would not give optimum operation and therefore zero-voltage-switching (ZVS) and zero-voltage-derivative switching (ZVDS) conditions should be applied instead. In the modified series-L/parallel-tuned Class-E configuration, the output-device inductance and the output-device output capacitance, both of which can significantly affect the amplifier's performance at microwave frequencies, furnish part, if not all, of the series inductance L and the shunt capacitance COUT, respectively. Further, when compared with the classic shunt-C/series-tuned topology, the proposed Class-E configuration offers some advantages in terms of 44% higher maximum operating frequency (fMAX) and 4% higher power-output capability (PMAX). As in the classic topology, the fMAX of the proposed amplifier circuit is reached when the output-device output capacitance furnishes all of the capacitance COUT, for a given combination of frequency, output power and DC supply voltage. It is also shown that numerical simulations agree well with theoretical predictions.

Exploring figures of merit associated with the suboptimum operation of class-E power amplifiers

Closed-form design equations for the operation of a class-E amplifier for zero switch voltage slope and arbitrary duty cycle are derived. This approach allows an additional degree of freedom in the design of class-E amplifiers which are normally designed for 50 duty ratio. The analysis developed permits the selection of non-unique solutions where amplifier efficiency is theoretically 100 but power output capability is less than that the 50 duty ratio case would permit. To facilitate comparison between 50 (optimal) and non-50 (suboptimal) duty ratio cases, each important amplifier parameter is normalised to its corresponding optimum operation value. It is shown that by choosing a non-50 suboptimal solution, the operating frequency of a class-E amplifier can be extended. In addition, it is shown that by operating the amplifier in the suboptimal regime, other amplifier parameters, for example, transistor output capacitance or peak switch voltage, can be included along with the standard specification criteria of output power, DC supply voltage and operating frequency as additional input design specifications. Suboptimum class-E operation may have potential advantages for monolithic microwave integrated circuit realisation as lower inductance values (lower series resistance, higher self-resonance frequency, less area) may be required when compared with the results obtained for optimal class-E amplifier synthesis. The theoretical analysis conducted here was verified by harmonic balance simulation, with excellent agreement between both methods. © The Institution of Engineering and Technology 2007.

A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

Graphical determination of network limits for wind power integration

A graphical method is presented for determining the capability of individual system nodes to accommodate wind power generation. The method is based upon constructing a capability chart for each node at which a wind farm is to be connected. The capability chart defines the domain of allowable power injections at the candidate node, subject to constraints imposed by voltage limits, voltage stability and equipment capability limits being satisfied. The chart is first derived for a two-bus model, before being extended to a multi-node power system. The graphical method is employed to derive the chart for a two-node system, as well as its application to a multi-node power system, considering the IEEE 30-bus test system as a case study. Although the proposed method is derived with the intention of determining the wind farm capacity to be connected at a specific node, it can be used for the analysis of a PQ bus loading as well as generation.

Digital Baseband Predistortion Based Linearized Broadband Inverse Class-E Power Amplifier

A newly introduced inverse class-E power amplifier (PA) was designed, simulated, fabricated, and characterized. The PA operated at 2.26 GHz and delivered 20.4-dBm output power with peak drain efficiency (DE) of 65% and power gain of 12 dB. Broadband performance was achieved across a 300-Mitz bandwidth with DE of better than 50% and 1-dB output-power flatness. The concept of enhanced injection predistortion with a capability to selectively suppress unwanted sub-frequency components and hence suitable for memory effects minimization is described coupled with a new technique that facilitates an accurate measurement of the phase of the third-order intermodulation (IM3) products. A robust iterative computational algorithm proposed in this paper dispenses with the need for manual tuning of amplitude and phase of the IM3 injected signals as commonly employed in the previous publications. The constructed inverse class-E PA was subjected to a nonconstant envelope 16 quadrature amplitude modulation signal and was linearized using combined lookup table (LUT) and enhanced injection technique from which superior properties from each technique can be simultaneously adopted. The proposed method resulted in 0.7% measured error vector magnitude (in rms) and 34-dB adjacent channel leakage power ratio improvement, which was 10 dB better than that achieved using the LUT predistortion alone.

Harnessing the power of personality assessment:Subjective assessment predicts behaviour in horses

Objective assessment of animal personality is typically time consuming, requiring the repeated measure of behavioural responses. By contrast, subjective assessment of personality allows information to be collected quickly by experienced caregivers. However, subjective assessment must predict behaviour to be valid. Comparisons of subjective assessments and behaviour have been made but often with methodological weaknesses and thus, limited success. Here we test the validity of a subjective assessment against a battery of behaviour tests in 146 horses (Equus caballus). Our first aim was to determine if subjective personality assessment could predict behaviour during behaviour testing. We made specific a priori predictions for how subjectively measured personality should relate to behaviour testing. We found that Extroversion predicted time to complete a handling test and refusal behaviour during this test. It also predicted minimum distance to a novel object. Neuroticism predicted how reactive an individual was to a sudden visual stimulus but not how quickly it recovered from this. Agreeableness did not predict any behaviour during testing. There were several unpredicted correlations between subjective measures and behaviour tests which we explore further. Our second aim was to combine data from the subjective assessment and behaviour tests to gain a more comprehensive understanding of personality. We found that the combination of methods provides new insights into horse behaviour. Furthermore, our data are consistent with the idea of horses showing different coping styles, a novel finding for this species. © 2013 Elsevier B.V.

Flexible fault ride through strategy for wind farm clusters in power systems with high wind power penetration

This paper investigates a flexible fault ride through strategy for power systems in China with high wind power penetration. The strategy comprises of adaptive fault ride through requirements and maximum power restrictions of the wind farms with weak fault ride through capabilities. The slight faults and moderate faults with high probability are the main defending objective of the strategy. The adaptive fault ride through requirement in the strategy consists of two sub fault ride through requirements, a temporary slight voltage ride through requirement corresponding to a slight fault incident, with a moderate voltage ride through requirement corresponding to a moderate fault. The temporary overloading capability of the wind farm is reflected in both requirements to enhance the capability to defend slight faults and to avoid tripping when the crowbar is disconnected after moderate faults are cleared. For those wind farms that cannot meet the adaptive fault ride through requirement, restrictions are put on the maximum power output. Simulation results show that the flexible fault ride through strategy increases the fault ride through capability of the wind farm clusters and reduces the wind power curtailment during faults.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.