Relaying technologies for smart grid

The hybrid wireless and power line communication (PLC) technology greatly improves the reliability of Power Internet of Things network (IoT) and the access ability of the terminals. However, with the development of Power IoT, the topology structure of the network becomes exceedingly complex, and the difficulty of networking is also gradually increasing. To address this issue, this paper proposes a multi-ant colony (MACS) based QoS networking algorithm for hybrid PLC and wireless communication networks. Considering the network QoS parameters and service demands, the networking algorithm constructs the fitness function to evaluate the quality of communication transmission paths, and the MACS algorithm is employed to establish the optimal relay route to complete networking. Numerical results prove that the MACS algorithm has a superior ability to jump out of local optimum than the traditional improved ant colony system (IACS) algorithm. Moreover, our proposed algorithm can construct the optimal relay route under corresponding constraints and demands according to different types of services.

In order to improve the coverage and reliability of the information perception layer of the power Internet of things, a method of constructing a cross layer fusion network of low-voltage power line and micro-power wireless communication (CPW) is proposed. Firstly, a unified medium access control (MAC) layer model of CPW is established to provide basic support for realizing the integration of CPW network layers; then an improved ant colony algorithm combining Brownian motion and local convergence times control is proposed to complete the networking process of CPW; Distribute the sub-service flow of CPW, and propose the bit error rate requirement factor in the service distribution, and accomplish the cross-layer integration of low-voltage power line and micro-power wireless communication network. The simulation results show that the communication link service quality of the cross-layer fusion network is better than that of the power line and wireless dual-mode or cascaded communication networks. The communication efficiency of CPW is improved to 398.20kbits/s. The coverage of the power line and wireless communication fusion network can be expanded by setting corresponding bit error rate demand factors according to different services. It takes into account the balance of communication link quality and network load.

Exponential demand for broadband applications has elicited renewed interest in characterizing noise in power line communication (PLC) networks for optimal error performance analysis. PLC is economical and ubiquitous, however due to multipath phenomena and nonstationary impulsive noise, the PLC channel is very harsh to signal transmission. Recent studies reveal that PLC noise is cyclostationary and modelling its characteristic should exploit the spectral and temporal redundancy. In addition, preliminary research shows that PLC noise exhibits self-similarity and can be predicted by fractal theory. In modelling noise employing fractal theory, the Hausdorff dimension (D) needs to be approximated. D is related to Hurst parameter (H), which is a measure of self-similarity. Hence, it is important to estimate H for accurate noise prediction using fractal theory. H parameter estimators assume stationarity in time series data. Therefore, preprocessing is required for the case of PLC noise before estimating H to make it stationary as periodicity and nonstatioanrity affects the H estimators. In this study, we remove the periodicity hidden in the PLC noise obtained from measurement campaign within several university offices, workshops and laboratories. Then long-range dependence is estimated from this pre-processed noise data by employing three estimation algorithms: R/S (rescaled adjusted range) method, aggregated variance method and absolute values of aggregated series method. Results from the said estimation methods though not the same, show that the Hurst parameters obtained from various noise measurements are greater than 0.5, implying presence of self-similarity with long-term memory. Results from this study will find valuable applications in PLC noise modelling using fractal and chaos theory and in error performance analysis in both narrowband and broadband PLC systems.

Hybridisation of networks is a trend that will continue to evolve for more efficient communication solutions. Hybrid power line communication (PLC) and wireless networks deployed for the Internet of Things (IoT) applications have improved the efficiency of low-power and lossy networks (LLNs). State-of-the-art solutions on hybrid networks are mainly application specific and does not provide a mathematical model to evaluate the network performance. Furthermore, the parallel PLC and wireless networks result in more power consumption due to the additional circuitry required to support and switch between two networks. There is a need for a generalized study on hybrid network architectures. A mathematical model must be developed to evaluate the performance of hybrid networks. Unlike parallel PLC and wireless networks, some intermediate solutions must be proposed that can offer the benefits of wired and wireless networks without increasing power consumption and circuit complexity. The paper presents a generalized study on hybrid network architectures. A hybrid network has been proposed that can offer the benefits of wired and wireless networks in the best possible way. A novel Markov-based stochastic model has been developed to evaluate the reliability of hybrid network designs. The analytical results obtained from Continuous Time Markov Chains (CTMC) have been validated by simulation using MATLAB. Spectral and energy efficiency have been evaluated for the hybrid designs along with a trade-off analysis. The proposed hybrid network (with 50% jump nodes being hybridized) is 33.53% more reliable for data transmission than parallel wired and wireless networks after 100 hours of operation.

As one of the most promising networking technologies, power-line communication (PLC) networks have been gradually used not only in home networking systems but also in industrial IoT (Internet of things). Current studies of PLC medium access control (MAC) protocol (i.e., IEEE 1901) only focus on the single-hop environment; however, in practical industrial IoT and home access communication systems, PLC networks generally utilize a multi-hop architecture. In addition, due to the difference in the MAC standard between 802 series and 1901, existing analytical models of multi-hop IEEE 802.11 wireless networks are not suitable for multi-hop IEEE 1901 PLC networks. In this paper, we propose a theoretical model for performance analysis of multi-hop IEEE 1901 PLC networks, where the impacts of traffic rate (containing relay traffic), buffer size, deferral counter process of 1901, hidden terminal problem and multi-hop environment are comprehensively considered. The modeling process is divided into two parts. In the local modeling part, we construct a brand-new Markov chain model to investigate the carrier sense multiple access with collision avoidance process of IEEE 1901 protocol under the multi-hop environment. In the coupling queueing modeling part, we employ queueing theory to analyze the packet transmission procedure between successive hops. On the basis, we derive the closed-form expressions of throughput, medium access delay, packet blocking probability, end-to-end successful transmission delay and goodput. Through extensive simulations, we verify that our theoretical model can accurately estimate the MAC performance of IEEE 1901 PLC networks in the multi-hop environment.

The metal halide lamp is a high energy electric lamp that produces visible light by an electric arc tube and it is a type of high-intensity discharge (HID) that contains a fused quartz and mixture of gases. These lamps inject noise into the smart-grid power line communications (PLC) network. This can have a strong and negative effect when using the PLC system to control the automatic switching of lamps in public places. In this paper we investigate the effects when the metal halide lamps with electronic or electromagnetic ballasts are seen as noise sources on the smart-grid power line network. It is shown that in the CENELEC band: (3 kHz–150 kHz) the interference level from metal halide lamps is significantly below the allowed maximum PLC signal levels. In the band 150 kHz–30 MHz however, PLC signals compete with Electromagnetic Compatibility (EMC) levels. The operational methods of the electronic and electromagnetic ballasts when connected to the metal halide lamps are explained.

Today, and especially in the future, there is an increasing need for complementary techniques to meet the growing capacity requirements of wireless communications. A high data rate connection with power line communication (PLC) is one attractive solution to achieve this need. One way to improve the data rates obtained in PLC networks is to increase the used bandwidth and operate at higher frequencies. At the same time, however, it should be kept in mind that the power lines, unfortunately, act as quite good antennas which can detect strong near-by FM and TV signals. In the future, the PLC frequency range can overlap with the broadcasting frequency bands resulting in interference for these transmissions and vice versa. With the static notching the whole FM-frequency band is notched during the PLC communication. This may result in inefficient spectrum utilization as in a given place and at a given time only a part of the FM-frequency band may be occupied. In this paper, a blind spectrum sensing algorithm based on forward consecutive mean excision (FCME) algorithm is proposed to locate the free frequency bands inside the broadcasting frequency range in PLC-network. The performance of this sensing algorithm is analyzed using spectrum analyzer measurement results and calculations in the broadcasting frequency range. The results verify that the proposed method performs well when detecting free frequency bands in broadcasting frequency band providing means for better spectrum utilization in PLC-network.

Power line Communications (PLC) has gained a lot of interest for the last mile or access because the normal electric power line is utilized for transmission also communication signals. Considerable research effort has been extended on investigating the technologies about the physical (PHY) layer and the medium access control (MAC) layer. But the transmissions scheduling is still a key design problem, especially in multi-hop PLC networks. In this paper, a time slot reuse schedule (TSRS) is proposed, to scheduled access in the networks. It utilizes time slots assignment with special reuse for the nodes in the cell, to improve the channel efficiency. TSRS is an appealing concept for PLC networks, since there is always a manager controlling the network, and PLC networks are often master slave structure with tree topologies. For successfully preventing the potential collision between the nodes, the performance of networks is improved significantly, which is also corroborated by the simulation results.

There is a need to fulfill the growing capacity requirements of power line communication (PLC). One way to achieve higher data rates in PLC networks is to operate at higher frequencies and increase the used bandwidth. In the future, the increased PLC frequency range can overlap with several wireless communications systems resulting in interference for these transmissions and vice versa. Commonly used static notching results in inefficient spectrum utilization as in a given place and at a given time only a part of the frequency range may be occupied by other wireless systems. Some wireless signals like FM signals are very closely spaced. In practice it is impossible to utilize these narrowband unoccupied frequency bands. In this case we can cluster closely spaced signals. Guard bands around detected wireless signals must also be taken into account before defining usable frequency bands for PLC. In this paper, we use the 2-D LAD ACC algorithm to detect and cluster interfering signals. We also further develop the algorithm so that after clustering guard bands are added and reclustering, if necessary, is executed. The performance of this modified 2-D LAD ACC algorithm is analyzed using calculations and spectrum analyzer measurement results obtained from in-office PLC network in the frequency range 80–200 MHz. The results verify that the proposed method performs well by detecting and clustering continuous and non-continuous wireless signals and providing means for clever spectrum utilization in PLC-network.

Power line communication (PLC) over low-voltage grid, is an attractive technology for no-new-wiring networks. In such typical application fields as home networks and street lighting management system, PLC network is always vulnerable in robustness and the logical link is liable to be destroyed because of the different user's personalities and the special channel properties. To improve the robustness of PLC network, the reasons that lead to the failure of data link are revealed, and the main achievements of randomized initialization protocols for ad hoc networks are studied. According to the study results, a novel low cost intelligent control method in MAC protocol is proposed to automatically maintain the logical connectivity of the PLC network. The performance of the method is evaluated theoretically and experimentally.

Power line communication is a critical component of smart grids, which are vulnerable to eavesdropping. To address this challenge, we investigate a cooperative relay hybrid power line and wireless communication system where multiple eavesdroppers are considered. We propose an elaborate artificial noise (AN)-aided beamforming (BF) scheme to improve physical layer security. Our scheme maximizes the outage-constrained secrecy rate (OCSR) of the legitimate link while restricting the capacity of the eavesdroppers to a reasonable region. However, due to the imperfect channel state information of the wiretap channel and secrecy outage probability constraint, the robust OCSR problem becomes intractable because of the non-concave secrecy objective function and the non-convex constraints. To solve this issue, we utilize semidefinite programming and Bernstein-type inequality to transform the robust OCSR nonconvex problem into two convex sub-problems, which a block-coordinated descent algorithm can solve. Simulation results showcase the effectiveness of our robust AN-aided secure BF scheme and show that the proposed scheme outperforms the benchmark scheme in a security performance gain under various channel conditions, even in the worst case.

Aiming at the problems of traditional wireless communication network security vulnerability monitoring systems such as low monitoring accuracy and time-consuming, a machine learning-based intelligent monitoring system for wireless communication network security vulnerabilities is proposed. In the hardware design of the monitoring system, based on the overall architecture of the wireless communication network and the data characteristics of the wireless communication network, it is divided into a vulnerability data collection module, a vulnerability data scanning module, and a network security vulnerability intelligent monitoring module. In the vulnerability data collection module, the wireless data collector is used to collect vulnerability data in the vulnerability database, and according to the attributes of the vulnerability data, the XSS vulnerability detection plug-in is connected to the vulnerability scanner to scan for wireless communication network vulnerabilities; When the communication network vulnerability data signal is traced, the system session operation of monitoring the vulnerability data. The software part introduces the neural network algorithm in the machine learning intelligent algorithm to process the hidden data in the security vulnerability data. The experimental results show that the wireless communication network security vulnerability intelligent monitoring system based on machine learning can effectively improve the system monitoring accuracy and the efficiency of wireless communication network security vulnerability monitoring.

Hybrid communication of power line and wireless can improve the performance without dedicated communication lines and meet the requirements of in-vehicle communication. Aiming at the medium access control (MAC) design of dual-connection architecture, we propose a parallel access scheme for power line and wireless hybrid communication suitable for parallel diversity transmission, and establish transmission and collision probability equations on the basis that the two channel conditions are consistent. Further, the influences of the carrier sense multiple access with collision avoidance (CSMA/CA) and time division multiple access (TDMA) on the performance of the parallel access scheme are analyzed. Based on numerical results, it is shown that the proposed scheme can significantly improve the performance of throughput and delay when compared to power line and wireless network alone.

Base station location selection and network optimization are critical to improving the performance of wireless communication networks in terms of latency reduction. To this end, the article proposes leveraging a convolutional neural network (CNN) to improve the accuracy of base station location selection and network latency reduction. The CNN method, based on a three-dimensional representation including signal strength data set, network topology data set, and transmission path data set, is used to select base station location and optimize the multihop relay network for latency reduction. The article presents a following method: location selection and network optimization for the wireless communication network. First, it collects the experimental data set of base station location selection and network optimization, and then uses the training data to train the CNN model to extract features. Once the training is done, the article further optimizes the network parameters and configurations, and ultimately obtains the optimal base station location and network configuration while minimizing network latency. As a result, simulation results indicate that the CNN model has remarkable performance in base station location selection, as well as in network optimization. In summary, the feature extraction and processing ability of CNN are powerful, enabling it to effectively capture factors leading to delay, hence improving the performance of base station location selection and network optimization. The article also demonstrates that the CNN model can be adjusted according to different environments and scenario settings through dynamic tuning.

Power Line Communications (PLC) is regarded as the promising home networking technology for smart grid application since the PLC technology enables data transmission via the existing power line. The cooperation between PLC and Wireless Local Area Network (WLAN) will be an effective hybrid solution for Home Area Networks (HANs). In this paper, we consider that Transmission Control Protocol (TCP) packets are transmitting in heterogenous communications network based on PLC and WLAN. The existing TCP congestion control has low effectiveness due to the error-prone power line channel. Therefore, we propose an adaptive rate control to improve TCP performance in the hybrid PLC/WLAN HANs. We exploit the communications mechanism of WLAN to improve the PLC link utilization. The methodology of Heuristic Dynamic Programming (HDP) is employed to obtain the optimal rate policy. The bottleneck link capacity is estimated by neural network training. The sending rate is adapted according to the long-term quality of service (QoS) of applications. Simulation results illustrate that the proposed rate control effectively filters out the negative impact from the instantaneous impulse noise in PLC channel and outperforms the existing TCP.