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

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.

Under the framework of event-triggered transmission mechanism, the problem of attack detection and state estimation of multi-sensor linear time-invariant systems under static attacks is considered. First, for each transmission channel, the sensor collects measurement information according to an event-triggered mechanism to reduce unnecessary energy consumption. Then, inspired by the clustering algorithm in machine learning, a detection mechanism based on Gaussian mixture model, which can set a confidence level for the measurement of each sensor is proposed. Finally, centralised data fusion is performed according to the results of attack detection and event-triggered judgement to realise remote state estimation. A numerical example proves that the proposed algorithm can locate the damaged sensor, save the network transmission bandwidth under the premise of ensuring accuracy and efficiency of sensor estimation.

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.

The AC/DC hybrid microgrid, which integrates AC microgrid and DC microgrid, greatly improves the flexibility of distributed energy. However, it is imperative to acknowledge that the microgrid in question is accompanied by a significantly elevated network security risk. To solve this problem, this article proposes a false data injection attack detection and alarm method based on active power output. The detection algorithm is capable of detecting attacks at any location within the microgrid and mitigating the impact of communication delay. Moreover, a distributed elastic recovery strategy based on the aforementioned detection algorithm is proposed. This strategy aims to expeditiously restore the compromised agent to its rated state. Finally, a simulation model is developed to demonstrate the effectiveness of the proposed strategy.

Cyber-Physical Systems (CPSs) are becoming more automated and aimed to be as efficient as possible by enabling integration between their operations and Information Technology (IT) resources. In combination with production automation, these systems need to identify their assets and the correlation between them; any potential threats or failures alert the relevant user/department and suggest the appropriate remediation plan. Moreover, identifying critical assets in these systems is essential. With numerous research and technologies available, assessing IT assets nowadays can be straightforward to implement. However, there is one significant issue of evaluating operational technology critical assets since they have different characteristics, and traditional solutions cannot work efficiently. This study presents the necessary background to attain the appropriate approach for monitoring critical assets in CPSs' Situational Awareness (SA). Additionally, the study presents a broad survey supported by an in-depth review of previous works in three important aspects. First, it reviews the applicability of possible techniques, tools and solutions that can be used to collect detailed information from such systems. Secondly, it covers studies that were implemented to evaluate the criticality of assets in CPSs, demonstrates requirements for critical asset identification, explores different risks and failure techniques utilised in these systems and delves into approaches to evaluate such methods in energy systems. Finally, this paper highlights and analyses SA gaps based on existing solutions, provides future directions and discusses open research issues.

Spot pricing is often suggested as a method of increasing demand-side flexibility in electrical power load. However, few works have considered the vulnerability of spot pricing to financial fraud via false data injection (FDI) style attacks. The authors consider attacks which aim to alter the consumer load profile to exploit intraday price dips. The authors examine an anomaly detection protocol for cyber-attacks that seek to leverage spot prices for financial gain. In this way the authors outline a methodology for detecting attacks on industrial load smart meters. The authors first create a feature clustering model of the underlying business, segregated by business type. The authors then use these clusters to create an incentive-weighted anomaly detection protocol for false data attacks against load profiles. This clustering-based methodology incorporates both the load profile and spot pricing considerations for the detection of injected load profiles. To reduce false positives, the authors model incentive-based detection, which includes knowledge of spot prices, into the anomaly tracking, enabling the methodology to account for changes in the load profile which are unlikely to be attacks.

Non-effectively grounded power distribution systems (NGDS) are widely used in China and other countries. However, over a long time, single-phase-to-ground faults (SGF) have been misjudged or omitted by the monitoring system, threatening the security of the power supply system and human safety. Based on the reason analysis for the omitted and misjudged SGFs in NGDS, the concept of NGDS fault eigenparameters to correctly reflect fault characteristics and a method of SGF detection based on fault eigenparameters are proposed. Then, the detection mechanism of phase voltage and fault current resistive elements for SGF is revealed. The variation characteristics of typical fault parameters changing with distribution system scale (parameter) and fault transition resistance, such as residual voltage and Zero-sequence current (iA-iO), are analysed. The eigenparameters of SGF in certain NGDS with specific scales/parameters are also proposed, which can correctly reflect the fault characteristics under different transition resistances.