Optimizing 100%‐renewable grids through shifting residential water‐heater load

A low‐carbon electricity supply for Australia was simulated, and the installed capacity of the electrical grid was optimized by shifting the electricity demand of residential electric water heaters (EWHs). The load‐shifting potential of Australia was estimated for each hour of the simulation period using a nationwide aggregate EWH load model on a 90 × 110 raster grid. The electricity demand of water heaters was shifted from periods of low renewable resource and high demand to periods of high renewable resource and low demand, enabling us to effectively reduce the installed capacity requirements of a 100%‐renewable electricity grid. It was found that by shifting the EWH load by just 1 hour, the electricity demand of Australia could be met using purely renewable electricity at an installed capacity of 145 GW with a capacity factor of 30%, an electricity spillage of 20%, and a generation cost of 15.2 ¢/kWh. A breakdown of the primary energy sources used in our scenario is as follows: 43% wind, 29% concentrated solar thermal power, and 20% utility photovoltaic. Sensitivity analysis suggested that further reduction in installed capacity is possible by increasing the load‐shifting duration as well as the volume and insulation level of the EWH tank.     

Dynamic load-altering attack detection based on adaptive fading Kalman filter in power systems

This paper presents an effective and feasible method for detecting dynamic load-altering attacks (D-LAAs) in a smart grid. First, a smart grid discrete system model is established in view of D-LAAs. Second, an adaptive fading Kalman filter (AFKF) is designed for estimating the state of the smart grid. The AFKF can completely filter out the Gaussian noise of the power system, and obtain a more accurate state change curve (including consideration of the attack). A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs. Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity, especially for very weak D-LAA attacks. Finally, the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.     

Combined effect of antimicrobial edible coatings with reduction of initial microbial load on the shelf-life of fresh hake (Merluccius merluccius) medallions

Two distinct strategies were combined to preserve fresh fish (Merluccius merluccius) under refrigeration at 4 °C for 12 days: (i) the application of an antimicrobial edible coating enriched with oregano essential oil (OEO) or carvacrol (CV) and (ii) the reduction of initial microbial load by good handling practise and the use of sodium hypochlorite (NaOCl). The action of antimicrobial coatings alone retarded the growth of Enterobacteriaceae, lactic acid bacteria (LAB) and H₂S producing bacteria on fish samples. The reduction of initial microbial load by itself only affected the evolution of LAB, but not the rest of the bacterial groups. When using both techniques combined, edible antimicrobial coatings were significantly more effective with additional and significant delays in the growth of mesophilic, psychrotrophic and Pseudomonas bacteria. Thus, the use of both strategies combined resulted in a reduction of the counts of all bacterial groups after 12 days of storage which ranged from 1.5 log and 8 log, in Pseudomonas and H₂S producing bacteria, respectively. Moreover, no significant differences were observed when comparing the microbiological evolution of samples treated with OEO compared to those only treated with CV.     

Enhancing stability region of time‐delayed smart power grids by non‐integer controllers

In this study, the impacts of non‐integer order controller on the stable parameter space of the microgrid (MG) frequency control system with fixed communication time delay are investigated and discussed with the help of the stability boundary locus (SBL) method. This study proposes a non‐integer order controller for the load frequency control (LFC) of the MG systems. To that end, the load frequency model of the MG is formulated and then the characteristic equations of this model are obtained. Then, with the help of this characteristic equation, the stable parameter space of the non‐integer controller is determined with regard to different time delay (τ ) and fractional integral order values (α ) using the SBL method. In order to show the accuracy of the obtained stable parameter space, time domain and generalized modified Mikhailov (GMM) criterion studies are carried out for different values of (τ ) and (α ). According to the results obtained, the areas of stable parameter space according to different α values and τ = 1.6 are calculated as 444.8860 for α = 0.4 , 342.9728 for α = 0.7 , 259.3578 for α = 1 , 216.2541 for α = 1.3 and 159.6826 for α = 1.6 . In addition, the areas of stable parameter space according to different τ values and α = 1.4 are calculated as 784.5222 for τ = 1 , 106.3219 for τ = 2 , 29.6959 for τ = 3 and 11.5946 for τ = 4 . Despite the extreme variability arising from nature of resources that make up the MG, the designed non integer order controller with the values selected within the stable parameter space stably carries out LFC control of the MG.     

高可靠重载升降装置技术研究

传统大型升降装置体积大，升降速度低，同步性差、启停冲击大，可靠性低，不满足大型贵重负载的升降需求。通过机械、电子、液压一体化设计，对大型贵重负载重载升降装置的设计方案进行了对比论证，并给出了综合最优解,为某重要产品的设计提供了技术支撑。     

移动式计量装置验收专用负荷箱的研制

本项目着力研发一种移动式计量装置验收专用负荷箱,可依据现场实际情况将其接入到专变低压出线处,提供稳定的测试负荷电流,完成计量装置验收关键步骤。主要的技术内容:通过接入电容器组,提供计量装置验收带负荷测试所需的电流,按不同容量电容器组配置合适分组空气开关,保护电容器组,降低同时投入时的冲击电流。本项目制作的计量装置验收专用负荷箱将使用高强度箱体,并制作不同容量的负荷箱以满足不同的计量装置验收负荷要求,以提高计量装置验收工作质量和率,降低电能计量差错风险。     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

Selection of features from power theories to compose NILM datasets

The load disaggregation concept is gaining attention due to the increasing need for optimized energy utilization and detailed characterization of electricity consumption profiles, especially through Nonintrusive Load Monitoring (NILM) approaches. This occurs since knowledge about individualized consumption per appliance allows to create strategies striving for energy savings, improvement of energy efficiency, and creating energy awareness to consumers. Moreover, by using feature extraction to devise energy disaggregation, one can achieve accurate identification of electric appliances. However, even though several literature works propose distinct features to be utilized, no consensus exists in the literature about the most appropriate set of features that ensure high accuracy on load disaggregation. Thus, beyond presenting a critical analysis of some significant features often selected in the literature, this paper proposes identifying the most relevant ones considering collinearity and machine learning algorithms. The results show that high-performance metrics can be achieved with fewer features than usually adopted in the literature. Moreover, it is demonstrated that the Conservative Power Theory can offer the most representative features for appliance identification, leading to efficient power consumption disaggregation.