IET Cyber-Physical Systems: Theory and Applications最新文献

Electricity substitution is an effective measure for reducing pollutant emissions and promoting the consumption of renewable energy. The current research lacks a quantitative analysis method for the factors affecting regional electricity substitution. This article proposes a decomposition model of the factors affecting electricity substitution based on Logarithmic Mean Divisia Index method to expand the depth and breadth of electricity substitution. Results show that: (1) Macro-level electricity substitution in Guangdong province develops steadily, with 1694.93 × 10⁸ kWh of total electricity substitution quantity during the period 2006–2020. The electricity substitution quantity in production sector accounts for 83.92% of the total, which is much larger than that in household sector. (2) Sustained improvement of labour productivity gives the largest contribution (3592.32 × 10⁸ kWh) to the increase of electricity substitution in production sector during the period 2006–2020. Living standard effect has the largest contribution (452.21 × 10⁸ kWh) to the increase of electricity substitution in household sector during the period 2006–2020. Electrification level effect and population effect have significant impact on electricity substitution development, while industrial structure has little impact on electricity substitution. (3) Energy intensity effect and energy consumption growth effect negatively influence electricity substitution in Guangdong province. The decline of energy intensity in the production sector has driven the decrease of electricity substitution quantity, contributing −2540.36 × 10⁸ kWh to electricity substitution during the period 2006–2020. Policy implications like continuously promoting breakthroughs in electricity substitution technologies, improvement of electrification rate in the sectors with potential for substitution, and improvement the price mechanism for fossil fuels and electricity can promote the deepening development of electricity substitution and the realisation of the carbon neutrality goal.

This article studies a distributed consensus-based estimation problem for discrete time-varying non-linear systems with missing measurements and Denial of Service (DoS) attacks. The probability of missing measurements is independent for each sensor. The communication link between sensor nodes is unreliable and subjected to DoS attacks. To achieve accurate state estimation against missing measurements, a local estimator with compensation mechanism is designed for each sensor node. A stochastic event-triggered mechanism is used to lessen additional information transfer. Based on this, a distributed consensus-based estimator is constructed by continually fusing local neighbours information matrixs and vectors. Moreover, the analysis of the designed estimator boundedness is presented. Finally, the effectiveness of the proposed algorithm is verified by three numerical examples.

A non-linear large scale stochastic optimisation model for enhancing the oil production and the recovery factor of the offshore oil reservoirs is proposed. The model aims at minimising the miss-match between mathematical model and the actual dynamic behaviour of the reservoir and the exploitation time, while maximising the oil production and the recovery factor. The model involves the three dimension (3D) oil reservoirs equipped with a few vertical injection and production wells. The limited number of wells is one of the major features of the common oil reservoirs in the middle-east region. The proposed model consists of the primarily mathematical model of the 3D reservoir, a model update algorithm and a large scale constrained non-linear optimisation algorithm. The input to this model is the daily production rate of the oil, natural gas and water produced from the oil reservoir and the output is the optimal injection rate to be injected to the injection wells in order to maximise the oil production and the recovery factor. In order to evaluate the performance of this model, the authors apply this model on part of one of the Iran's offshore oil reservoirs and study the performance improvement due to the proposed model and compare its performance with the performance of the available Improved Oil Recovery (IOR) technique. It is illustrated that the proposed model can increase the oil production from the reservoir up to 47.96% and reduce the exploitation period up to 66.66% compared with those of the available technique.

With the emergence of powerful and low-energy Internet of Things devices, deep learning computing is increasingly applied to resource-constrained edge devices. However, the mismatch between hardware devices with low computing capacity and the increasing complexity of Deep Neural Network models, as well as the growing real-time requirements, bring challenges to the design and deployment of deep learning models. For example, autonomous driving technologies rely on real-time object detection of the environment, which cannot tolerate the extra latency of sending data to the cloud, processing and then sending the results back to edge devices. Many studies aim to find innovative ways to reduce the size of deep learning models, the number of Floating-point Operations per Second, and the time overhead of inference. Neural Architecture Search (NAS) makes it possible to automatically generate efficient neural network models. The authors summarise the existing NAS methods on resource-constrained devices and categorise them according to single-objective or multi-objective optimisation. We review the search space, the search algorithm and the constraints of NAS on hardware devices. We also explore the challenges and open problems of hardware NAS.

In order to achieve more efficient and optimised resource scheduling, this research carried out a multi-objective task resource allocation method for low-voltage station edge computing based on hierarchical Bayesian adaptive sparsity. Based on hierarchical Bayesian adaptive sparsity, the multi-objective task resource allocation technical framework for edge computing in low-voltage stations is established, which is composed of end pipe edge cloud; After collecting real-time operation data of power distribution equipment, substation terminals, transmission terminals, etc. in the architecture end, it is transmitted to the data middle platform and service middle platform of the Internet of Things management platform in the cloud through the edge Internet of Things agent; Set and solve the constraint conditions, and build a multi type flexible load hierarchical optimal allocation model; The abnormal area topology identification sub module of multi-objective task resource of low-voltage station area edge computing is used to identify the abnormal area topology of the current low-voltage station area; Taking it as input, the multi-objective task resources of edge computing are allocated, and the multi-objective task resources allocation method of edge computing in low pressure platform area is realized under the differential evolution algorithm. The experimental results show that the proposed method has good convergence effect, strong distribution ability, relatively gentle increase in energy consumption, and the calculated results are basically consistent with the actual values, with good effectiveness.

A Cyber-Physical System (CPS) is a spatiotemporal multi-dimensional and heterogeneous hybrid autonomous system composed of deep integration of information resources and physical systems. With the development of digitisation and digitalisation, a large number of data acquisition equipment, computing equipment, and electrical equipment are interconnected between the power grid and the information communication network. The power grid has thus been restructured as a mature and highly complex CPS. In order to promote the development of power grid CPS technologies and provide a reference for relevant researchers in the field, the origin and concept of CPS and features in power grid CPS are introduced firstly. Then the key technologies of power grid CPS simulation are discussed and further analysed from three perspectives, including modelling theory, simulation methods, and system-level simulation. On this basis, the application of CPS simulation technology in future power grids has been prospected.

False Data Injection (FDI) attacks can be injected into the communication links of microgrids and result in significant damages. An approach to detect false data injection (FDI) attacks is introduced and a method to maintain the average voltages of buses within an acceptable range is suggested. To this aim, the impacts of FDI are first studied and a detection mechanism is extracted based on the attack effects. Then, multiple local integral feedback is employed to maintain the voltages within a permitted interval. Next, the procedure to return the values to their initial conditions (following the removal of the attack) is addressed. Finally, the proposed methods are simulated by multiple attack scenarios and validated using experimental tests.

With the vigorous development of the energy Internet, all kinds of user information data are increasing day by day. How to comprehensively and deeply mine the effective information of users, develop a model to predict the behaviour characteristics of big data users, distinguish customer relationships, and provide an accurate basis for the next behaviour of users for various platforms have become one of the research hotspots of big data analysis of user behaviour. The data is sampled according to the feature vector of power user. The portrait mining of power user is conducted, and the user screening and analysis are conducted by using the measure of decision tree node purity in the model. The decision tree variable of the up–down stopping rule is generated. Then the results of the model and the Logistics model are tested and analysed, which can effectively predict the behaviour of power user. The proposed user strategy based on the characteristics of power consumption behaviour is analysed to verify the effectiveness of the scheme. The example shows that the model has a strong ability to distinguish and good stability than the traditional Logistics model, which can effectively predict the user's behaviour in advance, reduce user complaints, and help enterprises and users to form a long-term mechanism of mutual benefit and reciprocity, which has a strong practical significance. This paper analyses the panorama of users through power big data technology and proposes a maturity model to evaluate the priority of users' electricity consumption. It emphasises the use of resources and methods provided in the power big data technology package to solve the practical problems of users' electricity consumption, and helps power companies to avoid market risks and improve service levels, which has strong practical significance.

The massive perception data based on efficient analysis and intelligent decision have put forward higher requirements for high-precision time synchronisation with the construction and development of smart power grid. However, multi-reference source time-frequency synchronisation of power system only selects the best method after comparison, which cannot make the most efficient use of the existing resources. It also cannot meet the need for high-precision time synchronisation of future power system. The existing multi-reference source synthesis algorithms cannot take into account both long-term stability and high-precision synchronous output. This article presents a multi-reference source weighted improved noise model and the high-precision output method. The multi-reference source error after classification is eliminated by leading into classification vector and classification coefficient. The synthesised frequency offset or the time precision of output can be optimised as the objective function by weighted classification algorithm and genetic algorithm. A simulation example based on the synthesis of two satellite system clock sources and three local caesium reference sources shows that the peak value of long-term output accuracy is controlled within 10 ns after classification weighted synthesis and optimisation, which is better than that of any single reference source.