Fast depleting fossil fuels and the growing awareness for environmental protection have led us to the energy crisis. Hence, efforts are being made by researchers to investigate new ways to extract energy from renewable sources. ‘Microgrids’ with Distributed Generators (DG) are being implemented with renewable energy systems. Optimization methods justify the cost of investment of a microgrid by enabling economic and reliable utilization of the resources. This paper strives to bring to light the concept of Hybrid Renewable Energy Systems (HRES) and state of art application of optimization tools and techniques to microgrids, integrating renewable energies. With an extensive literature survey on HRES, a framework of diverse objectives has been outlined for which optimization approaches were applied to empower the microgrid. A review of modelling and applications of renewable energy generation and storage sources is also presented.

Optimization in microgrids with hybrid energy systems – A review

A magnetic induction tomography (MIT) apparatus comprises an excitation signal generator (70) for generating an excitation signal; a primary excitation coil (50) arranged to receive the excitation signal from the excitation signal generator (70) and to convert the excitation signal into electromagnetic radiation and to emit said radiation to excite a sample having at least one of an electrical conductivity distribution, an electrical permittivity distribution or a magnetic permeability distribution; a primary receiver coil (60) arranged to receive electromagnetic radiation from the excited sample and to convert the received radiation into a detection signal; and a signal distribution network (115) arranged to receive the detection signal from the primary receiver coil (60). The apparatus further comprises a passive reference detector arranged to detect the excitation signal and to convert the detected signal into a passive reference signal. The apparatus further comprises an active reference signal generator (230) for generating an active reference signal; and an active reference source (175) arranged to receive the active reference signal from the active reference signal generator (230) and to supply the active reference signal to the signal distribution network (115).

https://iopscience.iop.org/article/10.1088/0957-0233/12/8/319/pdf

Magnetic induction tomography

Partial discharge (PD) measurement has long been used as a test to evaluate different insulation system designs, and as a quality control test for new equipment. However, in the past 20 years, PD measurement has been widely applied to diagnose the condition of the electrical insulation in operating apparatus such as switchgear, transformers, cables, as well as motor and generator stator windings. Improvements in the capabilities as well the lower cost of sensors, electronics and memory is partly the reason for the increased popularity of PD diagnostics. Another reason has been the development of methods-including the use of ultrawide band detection-to improve the reliability of the PD measurement in the presence of noise. In addition, rapid progress is being made in automated pattern recognition techniques that also helps to suppress noise. This paper reviews the various PD measurement technologies that have been specifically developed to improve PD diagnostic methods, and outlines how they have been implemented for stators, cables, transformers and switchgear. Areas for further research are also presented.

Partial discharge diagnostics and electrical equipment insulation condition assessment

Assembly of 2D MXene sheets into a 3D macroscopic architecture is highly desirable to overcome the severe restacking problem of 2D MXene sheets and develop MXene‐based functional materials. However, unlike graphene, 3D MXene macroassembly directly from the individual 2D sheets is hard to achieve for the intrinsic property of MXene. Here a new gelation method is reported to prepare a 3D structured hydrogel from 2D MXene sheets that is assisted by graphene oxide and a suitable reductant. As a supercapacitor electrode, the hydrogel delivers a superb capacitance up to 370 F g−1 at 5 A g−1, and more promisingly, demonstrates an exceptionally high rate performance with the capacitance of 165 F g−1 even at 1000 A g−1. Moreover, using controllable drying processes, MXene hydrogels are transformed into different monoliths with structures ranging from a loosely organized porous aerogel to a dense solid. As a result, a 3D porous MXene aerogel shows excellent adsorption capacity to simultaneously remove various classes of organic liquids and heavy metal ions while the dense solid has excellent mechanical performance with a high Young's modulus and hardness.

/pdf/3d-macroscopic-architectures-from-self-assembled-mxene-1rf9fpdv42.pdf

3D Macroscopic Architectures from Self‐Assembled MXene Hydrogels

The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine supervisory control and data acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine CM is discussed.

/pdf/using-scada-data-for-wind-turbine-condition-monitoring-a-2xdkm0lwi0.pdf

Using SCADA data for wind turbine condition monitoring – a review

The objective of the paper is to discuss a tool which is proving extremely efficient in partial discharge measurement studies. Though the technique itself is not new, its application to partial discharge studies is. It will be demonstrated in this paper that it has tremendous power and this accounts for its rapid growth as an application in this field. The paper begins with the description of the fundamentals of wavelet analysis, wavelet categories and the properties of the associated wavelet transforms. PD pulses as acquired from detectors composed of different detection circuits are investigated and numerically simulated, and a method on how to select optimally the wavelet corresponding to the representative forms of PD pulse is then presented. Finally, applications of wavelet analysis to partial discharge studies are explored. The paper demonstrates that the wavelet based denoising method proposed in the paper can be employed in separating PD pulses from electrical noise successfully and can be used in pulse propagation studies of partial discharge in distributed impedance plant to provide enhanced information and further infer the original site of the PD pulse.

/pdf/interpretation-of-wavelet-analysis-and-its-application-in-bhbula5res.pdf

Interpretation of wavelet analysis and its application in partial discharge detection

Presents a discussion of some important and unresolved issues related to previous work to provide a more comprehensive understanding of the practicability of wavelet-based denoising.

Automated wavelet selection and thresholding for PD detection

This paper presents the latest development of an EMT system designed for use in the metal production industry such as imaging molten steel flow profiles during continuous casting. The system that has been developed is based on a commercial data acquisition board residing in a PC host computer and programmed in the LabView graphical language. The paper reviews the new EMT hardware electronics and software. The noise effects and the detectability limits of the system are given in the paper followed by the system sensitivity map analysis. Optimal image reconstructions, including the simultaneous iterative reconstruction technique (SIRT) and non-iterative Tikhonov regularization, truncated singular value decomposition (TSVD), are also discussed and applied for the system. The system has been demonstrated in real time (10 frames s−1 for 5 kHz excitation) with test phantoms that represent typical metal flow profiles such as central, annular stream and multiple streams.

https://iopscience.iop.org/article/10.1088/0957-0233/17/1/018/pdf

Hardware and software design for an electromagnetic induction tomography (EMT) system for high contrast metal process applications

An effective condition monitoring system of wind turbines generally requires installation of a high number of sensors and use of a high sampling frequency in particular for monitoring of the electrical components within a turbine, resulting in a large amount of data This can become a burden for condition monitoring and fault detection systems This paper aims to develop algorithms that will allow a reduced dataset to be used in wind turbine fault detection This paper first proposes a variable selection algorithm based on principal component analysis with multiple selection criteria in order to select a set of variables to target fault signals while still preserving the variation of data in the original dataset With the selected variables, this paper then describes fault detection and identification algorithms, which can identify faults, determine the corresponding time and location where the fault occurs, and estimate its severity The proposed algorithms are evaluated with simulation data from PSCAD/EMTDC, Supervisory control and data acquisition data from an operational wind farm, and experimental data from a wind turbine test rig Results show that the proposed methods can select a reduced set of variables with minimal information lost whilst detecting faults efficiently and effectively

/pdf/wind-turbine-fault-detection-and-identification-through-pca-1osj8mqdnv.pdf

Wind Turbine Fault Detection and Identification Through PCA-Based Optimal Variable Selection

Predictions of renewable energy (RE) generation and electricity load are critical to smart grid operation. However, the prediction task remains challenging due to the intermittent and chaotic character of RE sources, and the diverse user behavior and power consumers. This article presents a novel method for the prediction of RE generation and electricity load using improved stacked gated recurrent unit-recurrent neural network (GRU-RNN) for both univariate and multivariate scenarios. First, multiple sensitive monitoring parameters or historical electricity consumption data are selected according to the correlation analysis to form the input data. Second, a stacked GRU-RNN using a simplified GRU is constructed with improved training algorithm based on AdaGrad and adjustable momentum. The modified GRU-RNN structure and improved training method enhance training efficiency and robustness. Third, the stacked GRU-RNN is used to establish an accurate mapping between the selected variables and RE generation or electricity load due to its self-feedback connections and improved training mechanism. The proposed method is verified by using two experiments: prediction of wind power generation using multiple weather parameters and prediction of electricity load with historical energy consumption data. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods of machine learning or deep learning in achieving an accurate energy prediction for effective smart grid operation.

/pdf/a-stacked-gru-rnn-based-approach-for-predicting-renewable-5cs9zxcej9.pdf

Xiandong Ma

Papers

Interpretation of wavelet analysis and its application in partial discharge detection

Automated wavelet selection and thresholding for PD detection

Hardware and software design for an electromagnetic induction tomography (EMT) system for high contrast metal process applications

Wind Turbine Fault Detection and Identification Through PCA-Based Optimal Variable Selection

A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation