Time series wind power forecasting based on variant Gaussian process and TLBO

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.

Video anomaly detection in real-time on a Power-Aware Heterogeneous Platform

FPGAs and GPUs are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time and detection accuracy), and this decision is normally made once at design time. All three characteristics are important, particularly in battery-powered systems. Here we propose a method for moving selection of processing platform from a single design-time choice to a continuous run time one.We implement Histogram of Oriented Gradients (HOG) detectors for cars and people and Mixture of Gaussians (MoG) motion detectors running across FPGA, GPU and CPU in a heterogeneous system. We use this to detect illegally parked vehicles in urban scenes. Power, time and accuracy information for each detector is characterised. An anomaly measure is assigned to each detected object based on its trajectory and location, when compared to learned contextual movement patterns. This drives processor and implementation selection, so that scenes with high behavioural anomalies are processed with faster but more power hungry implementations, but routine or static time periods are processed with power-optimised, less accurate, slower versions. Real-time performance is evaluated on video datasets including i-LIDS. Compared to power-optimised static selection, automatic dynamic implementation mapping is 10% more accurate but draws 12W extra power in our testbed desktop system.

Damping Torque Analysis of Power Systems with DFIGs for Wind Power Generation

A grid-connected DFIG for wind power generation can affect power system small-signal angular stability in two ways: by changing the system load flow condition and dynamically interacting with synchronous generators (SGs). This paper presents the application of conventional method of damping torque analysis (DTA) to examine the effect of DFIG’s dynamic interactions with SGs on the small-signal angular stability. It shows that the effect is due to the dynamic variation of power exchange between the DFIG and power system and can be estimated approximately by the DTA. Consequently, if the DFIG is modelled as a constant power source when the effect of zero dynamic interactions is assumed, the impact of change of load flow brought about by the DFIG can be determined. Thus the total effect of DFIG can be estimated from the result of DTA added on that of constant power source model. Applications of the DTA method proposed in the paper are discussed. An example of multi-machine power systems with grid-connected DFIGs are presented to demonstrate and validate the DTA method proposed and conclusions obtained in the paper.

Improved electrochemical performance of Sr2Fe1.5Mo0.4Nb0.1O6-δ -Sm0.2Ce0.8O2-δ composite cathodes by a one-pot method for intermediate temperature solid oxide fuel cells

In this paper, Sr₂Fe_1.5Mo_0.4Nb_0.1O_6-δ (SFMNb)-xSm_0.2Ce_0.8O_2-δ (SDC) (x = 0, 20, 30, 40, 50 wt%) composite cathode materials were synthesized by a one-pot combustion method to improve the electrochemical performance of SFMNb cathode for intermediate temperature solid oxide fuel cells (IT-SOFCs). The fabrication of composite cathodes by adding SDC to SFMNb is conducive to providing extended electrochemical reaction zones for oxygen reduction reactions (ORR). X-ray diffraction (XRD) demonstrates that SFMNb is chemically compatible with SDC electrolytes at temperature up to 1100 °C. Scanning electron microscope (SEM) indicates that the SFMNb-SDC composite cathodes have a porous network nanostructure as well as the single phase SFMNb. The conductivity and thermal expansion coefficient of the composite cathodes decrease with the increased content of SDC, while the electrochemical impedance spectra (EIS) exhibits that SFMNb-40SDC composite cathode has optimal electrochemical performance with low polarization resistance (R_p) on the La_0.9Sr_0.1Ga_0.8Mg_0.2O₃ electrolyte. The R_p of the SFMNb-40SDC composite cathode is about 0.047 Ω cm² at 800 °C in air. A single cell with SFMNb-40SDC cathode also displays favorable discharge performance, whose maximum power density is 1.22 W cm^-2 at 800 °C. All results indicate that SFMNb-40SDC composite material is a promising cathode candidate for IT-SOFCs.

Synthesis of Pr0.6Sr0.4FeO 3-δ -xCe0.9Pr0.1O 2-δ cobalt-free composite cathodes by a one-pot method for intermediate-temperature solid oxide fuel cells

Cobalt-free composite cathodes consisting of Pr_0.6Sr_0.4FeO _3-δ -xCe_0.9Pr_0.1O _2-δ (PSFO-xCPO, x = 0-50 wt%) have been synthesized using a one-pot method. X-ray diffraction, scanning electron microscopy, thermal expansion coefficient, conductivity, and polarization resistance (R _P ) have been used to characterize the PSFO-xCPO cathodes. Furthermore the discharge performance of the Ni-SSZ/SSZ/GDC/PSFO-xCPO cells has been measured. The experimental results indicate that the PSFO-xCPO composite materials fully consist of PSFO and CPO phases and posses a porous microstructure. The conductivity of PSFO-xCPO decreases with the increase of CPO content, but R _P of PSFO-40CPO shows the smallest value amongst all the samples. The power density of single cells with a PSFO-40CPO composite cathode is significantly improved compared with that of the PSFO cathode, exhibiting 0.43, 0.75, 1.08 and 1.30 W cm^-2 at 650, 700, 750 and 800 °C, respectively. In addition, single cells with the PSFO-40CPO composite cathode show a stable performance with no obvious degradation over 100 h when operating at 750 °C.

A low overhead error confinement method based on application statistical characteristics

Reliability has emerged as a critical design constraint especially in memories. Designers are going to great lengths to guarantee fault free operation of the underlying silicon by adopting redundancy-based techniques, which essentially try to detect and correct every single error. However, such techniques come at a cost of large area, power and performance overheads which making many researchers to doubt their efficiency especially for error resilient systems where 100% accuracy is not always required. In this paper, we present an alternative method focusing on the confinement of the resulting output error induced by any reliability issues. By focusing on memory faults, rather than correcting every single error the proposed method exploits the statistical characteristics of any target application and replaces any erroneous data with the best available estimate of that data. To realize the proposed method a RISC processor is augmented with custom instructions and special-purpose functional units. We apply the method on the proposed enhanced processor by studying the statistical characteristics of the various algorithms involved in a popular multimedia application. Our experimental results show that in contrast to state-of-the-art fault tolerance approaches, we are able to reduce runtime and area overhead by 71.3% and 83.3% respectively.

Integrated Optimal Power Flow for Distribution Networks in Local and Urban Scales

This paper develops an integrated optimal power flow (OPF) tool for distribution networks in two spatial scales. In the local scale, the distribution network, the natural gas network, and the heat system are coordinated as a microgrid. In the urban scale, the impact of natural gas network is considered as constraints for the distribution network operation. The proposed approach incorporates unbalance three-phase electrical systems, natural gas systems, and combined cooling, heating, and power systems. The interactions among the above three energy systems are described by energy hub model combined with components capacity constraints. In order to efficiently accommodate the nonlinear constraint optimization problem, particle swarm optimization algorithm is employed to set the control variables in the OPF problem. Numerical studies indicate that by using the OPF method, the distribution network can be economically operated. Also, the tie-line power can be effectively managed.

Management of public water supply to reduce power cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.