Deep learning driven data analytics for smart grids

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonAs advanced metering infrastructure (AMI) and wide area monitoring systems (WAMSs) are being deployed rapidly and widely, the conventional power grid is transitioning towards the smart grid at an increasing speed. A number of smart metering devices and real-time monitoring systems are capable to generate a huge volume of data on a daily basis. However, a variety of generated data can be made full use of to advance the development of the smart grid through big data analytics, especially, deep learning. Thus, the thesis is focused on data analysis for smart grids from three different aspects. Firstly, a real-time data driven event detection method is presented, which is quite robust when dealing with corrupted and significantly noisy data of phase measurement units (PMUs). To be specific, the presented event detection method is based on a novel combination of random matrix theory (RMT) and Kalman filtering. Furthermore, a dynamic Kalman filtering technique is proposed through the adjustment of the measurement noise covariance matrix as the data conditioner of the presented method in order to condition PMU data. The experimental results show that the presented method is indeed quite robust in such practical situations that include significant levels of noisy or missing PMU data. Secondly, a short-term residential load forecasting method is proposed on the basis of deep learning and k-means clustering, which is capable to extract similarity of residential load effectively and perform prediction accurately at the individual residential level. Specifically, it makes full use of k-means clustering to extract similarity among residential load and deep learning to extract complex patterns of residential load. In addition, in order to improve the forecasting accuracy, a comprehensive feature expression strategy is utilised to describe load characteristics of each time step in detail. The experimental results suggest that the proposed method can achieve a high forecasting accuracy in terms of both root mean square error (RMSE) and mean absolute error (MAE). Thirdly, an online individual residential load forecasting method is developed based on a combination of deep learning and dynamic mirror descent (DMD), which is able to predict residential load in real time and adjust the prediction error over time in order to improve the prediction performance. More specifically, it firstly employs a long short term memory (LSTM) network to build a prediction model offline, and then applies it online with DMD correcting the prediction error. In order to increase the prediction accuracy, a comprehensive feature expression strategy is used to describe load characteristics at each time step in detail. The experimental results indicate that the developed method can obtain a high prediction accuracy in terms of both RMSE and MAE. To sum up, the proposed real-time event detection method contributes to the monitoring and operation of smart grids, while the proposed residential load forecasting methods contribute to the demand side response in smart grids.TDX-ASSIS

Adaptive individual residential load forecasting based on deep learning and dynamic mirror descent

With a growing penetration of renewable energy generation in the modern power networks, it has become highly challenging for network operators to balance electricity supply and demand. Residential load forecasting nowadays plays an increasingly important role in this aspect and facilitates various interactions between power networks and electricity users. While numerous research works have been proposed targeting at aggregate residential load forecasting, only a few efforts have been made towards individual residential load forecasting. The issue of volatility of individual residential load has never been addressed in forecasting. Thus, to fill this gap, this paper presents a deep learning method empowered with dynamic mirror descent for adaptive individual residential load forecasting. The proposed method is evaluated on a real-life Irish residential load dataset, and the experimental results show that it improves the prediction accuracy by 9.1% and 11.6% in the aspects of RMSE and MAE respectively in comparison with a benchmark method

Real higher-order Weyl photonic crystal

Higher-order Weyl semimetals are a family of recently predicted topological phases simultaneously showcasing unconventional properties derived from Weyl points, such as chiral anomaly, and multidimensional topological phenomena originating from higher-order topology. The higher-order Weyl semimetal phases, with their higher-order topology arising from quantized dipole or quadrupole bulk polarizations, have been demonstrated in phononics and circuits. Here, we experimentally discover a class of higher-order Weyl semimetal phase in a three-dimensional photonic crystal (PhC), exhibiting the concurrence of the surface and hinge Fermi arcs from the nonzero Chern number and the nontrivial generalized real Chern number, respectively, coined a real higher-order Weyl PhC. Notably, the projected two-dimensional subsystem with kz = 0 is a real Chern insulator, belonging to the Stiefel-Whitney class with real Bloch wavefunctions, which is distinguished fundamentally from the Chern class with complex Bloch wavefunctions. Our work offers an ideal photonic platform for exploring potential applications and material properties associated with the higher-order Weyl points and the Stiefel-Whitney class of topological phases

An adaptive forecasting method for the aggregated load with pattern matching

Electrical load forecasting plays a vital role in the operation of power system. In this paper, a novel adaptive short-term load forecasting method for the aggregated load is built. The proposed method consists of two stages: load forecast model preparation stage and adaptive load forecast model selection stage. In the first stage, based on historical load data of all consumers, the typical monthly load patterns are firstly identified in an optimal fashion with the aid of the cosine similarity. Then, for each identified monthly load pattern, a stacking ensemble learning method is proposed to train the load forecasting model. In the second stage, according to the similarity between individual load data of the latest month and the identified monthly load pattern, all the consumers are firstly classified into different groups where each group corresponds to a particular load pattern. Then, for each group, the corresponding trained load forecasting model is employed for short-term load forecast and the final forecast of the aggregated load is calculated as a simple aggregation of the produced load forecast for each group of consumers. Case studies conducted on open dataset show that, compared with the single forecasting model, the proposed adaptive load forecasting method can effectively improve the load forecasting accuracy

Repair of rotator cuff tears in patients aged 75 years and older: Does it make sense? A systematic review

BackgroundRotator cuff injuries are common, and morbidity increases with age. The asymptomatic full-thickness tear rate is 40% in the over 75-year-old population.PurposeThis study aimed to systematically review the literature on the outcomes of rotator cuff repair among >75 years old patients.Study designSystematic review.MethodsA systematic review of the literature was performed following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A literature search was performed in the electronic databases of PubMed, Medline, Embase, and The Cochrane Library. Studies in English evaluating repair of full-thickness rotator cuff tears in patients aged >75 years were included.ResultsSix studies were reviewed, including 311 patients (313 shoulders) treated with arthroscopic and/or open rotator cuff repair. Sixty-one patients were lost to follow-up, leaving 252 shoulders with outcome data. Patients in this age group demonstrated a significant improvement in the clinical and functional scores after rotator cuff repair, with a high satisfaction rate. The mean American Shoulder and Elbow Surgeons scores improved from 43.8 (range, 42.0–45.5) preoperatively to 85.3 (range, 84.0 to 86.5) postoperatively, and the mean Constant scores improved from 45.4 (range, 34.7–55.5) to 78.6 (range, 67.0–91.6). Pain, evaluated in all studies by the visual analog scale for pain, showed a significant improvement at the last follow-up compared with the mean preoperative score. Furthermore, range of motion and return to daily activities and sports gained marked improvements.ConclusionRotator cuff repair in patients aged >75 years could achieve high clinical success rates with good outcomes and pain relief. Although patients in this age group are at a high risk of retear, rotator cuff repair may offer a good option with significant functional and clinical improvement

Optimization of an Aqueous Enzymatic Method and Supercritical Carbon Dioxide Extraction for <i>Paeonia suffruticosa</i> Andr. Seed Oil Production Using Response Surface Methodology (RSM)

Peony seed oil, a type of tree nut oil, has attracted the attention of nutritionists for its rich nutritional content. The aim of this study was to extract oil from the peony seed utilizing green and efficient methods. Specifically, aqueous enzymatic extraction was optimized using the Plackett–Burman design combined with the mixture design to extract the optimal enzyme ratio of peony seed oil. When the dosage of enzymes was 10 mg protein/g peony seed, the optimal ratios of the dosages of papain, cellulase, and pectinase were 16.15%, 31.33%, and 52.53%, respectively. Subsequently, central composite design was adopted to optimize supercritical CO2 extraction to identify the process parameters of extracting residual oil from the residue of the aqueous enzymatic extraction. Almost 6.30% of peony seed oil could be obtained from the residue using continuous extraction for 1.58 h at 49.41 °C and 59.75 Mpa. After mixing the peony seed oil extracted by the two processes, its physicochemical indices were measured. Compared with commercial peony seed oil extracted based on the organic solvent leaching method, the elative density and iodine value were higher based on our approach, whereas the other indices showed no significant differences. Thus, the two-step strategy combining the aqueous enzymatic method and supercritical CO2 extraction can be effectively applied to peony seed oil production

A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering

Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (PMUs) are being rapidly deployed in order to further enhance situational awareness for power system operators. This paper presents a novel data-driven event detection approach based on random matrix theory (RMT) and Kalman filtering. A dynamic Kalman filtering technique is proposed to condition PMU data. Both simulated and real PMU data from the transmission system of Great Britain (GB) are utilized in order to validate the proposed event detection approach and the results show that the proposed approach is much more robust with regard to event detection when applied in practical situations

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef

Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near–infrared hyperspectral imaging (NIR–HSI) combined with two–dimensional correlation spectroscopy (2D–COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D–COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH2 in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136–1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D–COS analysis, and provided excellent results (R2C of 0.8141, R2P of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D–COS combined with NIR–HSI could be used as an effective method to monitor Ala content in beef

Real higher-order Weyl photonic crystal

Figure data and codes for the paper "Real higher-order Weyl photonic crystal", by Y. Pan et al