Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.Comment: An updated version of this manuscript has been accepted by the 2018 IEEE International Conference on Energy Internet (ICEI), Beijing, Chin

Mitigating Multi-Stage Cascading Failure by Reinforcement Learning

This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is presented and its challenges are investigated. The problem is then tackled by the RL based on DC-OPF (Optimal Power Flow). Designs of the key elements of the RL framework (rewards, states, etc.) are also discussed in detail. Experiments on the IEEE 118-bus system by both shallow and deep neural networks demonstrate promising results in terms of reduced system collapse rates.Comment: This paper has been accepted and presented in the IEEE ISGT-Asia conference in 201

Optimization of Battery Energy Storage to Improve Power System Oscillation Damping

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is setup based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of a faster convergence rate with matched solution qualities.Comment: This paper has been accepted by IEEE Transactions on Sustainable Energy and now still in online-publication phase, IEEE Transactions on Sustainable Energy. 201

Optimal Battery Energy Storage Placement for Transient Voltage Stability Enhancement

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is set up based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of fewer iterations for convergence with better solution qualities.Comment: This paper has been accepted by the 2019 IEEE PES General Meeting at Atlanta, GA in August 201

Design and Realisation of High Accuracy Emissivity Measurement Instruments for Radiation Thermometry

Emissivity is the quantity representing the radiative properties of materials that must be prior measured precisely to undertake accurate measurements for radiation thermometry. This work presents the development and validation of three emissivity measurement instruments to undertake studies on emissivity behaviours for materials with complex surface conditions from 200 to 1150 °C. These instruments aim to offer accurate emissivity references for use in non-contact temperature measurements and materials science

Photoelectrochemical and electrochemical ratiometric aptasensing: a case study of streptomycin

There has been much interest in constructing ratiometric sensors using different sensing techniques because of their synergistic effect, although the simultaneous collection of the signals is challenging. Herein, we propose a ratiometric aptasensing strategy based on the dual-detection model with a photoelectrochemical (PEC) “signalon” and an electrochemical (EC) “signal-off”. As a proof-of-concept study, CdTe quantum dots (CdTe QDs) and a methylene blue-labeled aptamer (MB-Apt) were used to generate PEC and EC signals in the sensing system. The target-induced conformational change of MB-Apt pushed MB away from the electrode, thereby decreasing the EC signal; at the same time, the reduced steric hindrance favored the restoration of the PEC signal from the CdTe QDs. Thus, this PEC-EC strategy can achieve the PEC “signal-on” and EC “signal-off” states simultaneously, as well as allowing quantitative analysis of the target based on the ratio of the current intensities. As a model application, an aptasensor fabricated for streptomycin detection showed a wide linear range from 0.03 to 100 μM with a detection limit of 10 nM (S/N = 3). The proposed sensing platform displayed superior analytical properties compared with methods based on PEC or EC alone. Our work provides an efficient dual-detection modelbased ratiometric strategy for advanced analysis, and paves the way to the simultaneous acquisition of signals

Transportation Density Reduction Caused by City Lockdowns Across the World during the COVID-19 Epidemic: From the View of High-resolution Remote Sensing Imagery

As the COVID-19 epidemic began to worsen in the first months of 2020, stringent lockdown policies were implemented in numerous cities throughout the world to control human transmission and mitigate its spread. Although transportation density reduction inside the city was felt subjectively, there has thus far been no objective and quantitative study of its variation to reflect the intracity population flows and their corresponding relationship with lockdown policy stringency from the view of remote sensing images with the high resolution under 1m. Accordingly, we here provide a quantitative investigation of the transportation density reduction before and after lockdown was implemented in six epicenter cities (Wuhan, Milan, Madrid, Paris, New York, and London) around the world during the COVID-19 epidemic, which is accomplished by extracting vehicles from the multi-temporal high-resolution remote sensing images. A novel vehicle detection model combining unsupervised vehicle candidate extraction and deep learning identification was specifically proposed for the images with the resolution of 0.5m. Our results indicate that transportation densities were reduced by an average of approximately 50% (and as much as 75.96%) in these six cities following lockdown. The influences on transportation density reduction rates are also highly correlated with policy stringency, with an R^2 value exceeding 0.83. Even within a specific city, the transportation density changes differed and tended to be distributed in accordance with the city's land-use patterns. Considering that public transportation was mostly reduced or even forbidden, our results indicate that city lockdown policies are effective at limiting human transmission within cities.Comment: 14 pages, 7 figures, submitted to IEEE JSTAR

Quantitative thermal imaging using single-pixel Si APD and MEMS mirror

Accurate quantitative temperature measurements are difficult to achieve using focal-plane array sensors. This is due to reflections inside the instrument and the difficulty of calibrating a matrix of pixels as identical radiation thermometers. Size-of-source effect (SSE), which is the dependence of an infrared temperature measurement on the area surrounding the target area, is a major contributor to this problem and cannot be reduced using glare stops. Measurements are affected by power received from outside the field-of-view (FOV), leading to increased measurement uncertainty. In this work, we present a micromechanical systems (MEMS) mirror based scanning thermal imaging camera with reduced measurement uncertainty compared to focal-plane array based systems. We demonstrate our flexible imaging approach using a Si avalanche photodiode (APD), which utilises high internal gain to enable the measurement of lower target temperatures with an effective wavelength of 1 µm and compare results with a Si photodiode. We compare measurements from our APD thermal imaging instrument against a commercial bolometer based focal-plane array camera. Our scanning approach results in a reduction in SSE related temperature error by 66 °C for the measurement of a spatially uniform 800 °C target when the target aperture diameter is increased from 10 to 20 mm. We also find that our APD instrument is capable of measuring target temperatures below 700 °C, over these near infrared wavelengths, with D* related measurement uncertainty of ± 0.5 °C

Low-Cost Hyperspectral Imaging with A Smartphone.

Recent advances in smartphone technologies have opened the door to the development of accessible, highly portable sensing tools capable of accurate and reliable data collection in a range of environmental settings. In this article, we introduce a low-cost smartphone-based hyperspectral imaging system that can convert a standard smartphone camera into a visible wavelength hyperspectral sensor for ca. £100. To the best of our knowledge, this represents the first smartphone capable of hyperspectral data collection without the need for extensive post processing. The Hyperspectral Smartphone's abilities are tested in a variety of environmental applications and its capabilities directly compared to the laboratory-based analogue from our previous research, as well as the wider existing literature. The Hyperspectral Smartphone is capable of accurate, laboratory- and field-based hyperspectral data collection, demonstrating the significant promise of both this device and smartphone-based hyperspectral imaging as a whole