Electronic Communications Systems Fundamentals Through Advanced

Ion Cyclotron Resonance Heating (ICRH) system has two different impedance matching systems for RF transmission. One is offline matching which has been used before applying the experimental shots. Another is online impedance matching which has been used during experimental shot. Offline matching network consists of two coarse tuner, static stubs and coarse phase shifter which are identical in both the transmission lines. There are motorized arrangement installed in each stubs and phase shifters. Both static and coarse stubs are used to vary length in order to match the source impedance with load impedance. Phase shifter is used for matching impedance via varying the phase of the power. Programmable Logic Controller (PLC) based instrumentation has been implemented for the system. Offline matching should be operated below 1 kHz frequency in order to move stepper motors using square pulses employed to motor controller. In existing system this operation has been carried out by VME instrumentation and control. In order to reduce load on VME, PLC based system has been designed and integrated with VME based DAC. SIMATIC Windows Control Center (WinCC) software has been used as SCADA i.e. front end user interface. WinCC SCADA can communicate with OPC (Open Process Control) server using different PLC signals. This paper describes technical details, design and development of PLC based offline impedance matching system using WinCC as SCADA. The developed system has proved accurate and reliable in use of application as per system requirement. The SCADA system also shows motor frequency and motor speed profile with upper and lower limits. It logs data at the end of operation by which it retrieve the last run status of each motor when it starts again.

Integration of PLC based offline impedance matching system for ICRH experiments

Problems In Electronics Including Lumped Constants Transmission Lines And High Frequencies

|Powerline communication (PLC) noise is the main cause of reduced performance and reliability of the communication channel. The major source of these noise bursts, which distort and degrade the communication signal, is the arbitrary plugging in and unplugging of electric devices from the electrical network. It is therefore important to perform statistical modelling of the PLC noise characteristics to enable the development and optimisation of reliable PLC systems. This paper presents the Variational Bayesian (VB) Gaussian Mixture (GM) modelling of the amplitude distribution of the indoor broadband PLC noise. In the proposed model, a fully Bayesian treatment is employed where the parameters of the GM model are assumed to be random variables. Consequently, prior distributions over the parameters are introduced. The VB criterion is used to determine the optimal number of components where the Bayesian information criterion emerges as a limiting case. To (cid:12)nd the parameters of the GM components, the variational-expectation maximisation algorithm is employed. Measurements from diﬀerent indoor PLC environments are then used to validate the model. Thereafter, performance analysis is carried out, and the VB framework is compared to the Maximum Likelihood (ML) estimate method. It is observed that while the ML model performs better when the amplitude distribution contains multiple peaks, the VB framework oﬀers high accuracy and good generalization to the measured data and is thus eﬀective in modelling the amplitude distribution of the PLC noise.

/pdf/variational-bayesian-learning-for-the-modelling-of-indoor-2l1j726q.pdf

Variational Bayesian Learning for the Modelling of Indoor Broadband Powerline Communication Impulsive Noise

This paper presents the modelling of the powerline communication (PLC) noise using the Gaussian mixture model (Gaussian MM), where the K-means algorithm is employed to initialise the expectation maximisation (EM) algorithm. The Gaussian MM has been shown to adequately model the unpredictable PLC noise amplitude distribution, which is the primary cause of reduced PLC performance. However, the EM algorithm used in the estimation of the maximum likelihood parameters is known to be highly sensitive to the initial parameters. As a result, the K-means algorithm is proposed as a means of initializing the parameters. The resulting Gaussian MM is then validated through measurements and is observed to provide a good fit to the measured data. The performance of the K-means initialised model is then compared to the randomly initialised Gaussian MM. It is observed that the K-means initialised Gaussian MM generally performs better, although the difference in performance with the randomly initialised model is minimal. 

K-Means Initialisation for the Modelling of PLC Noise in Indoor Environment

Power line communication(PLC) is a wired communication technology that has recently received a lot of attention due to its attractive prospects towards home and /or neighborhood network applications as well as smart grid technologies. It allows establishing digital communications without any additional wiring requirements. Effectively, one’s home and/or neighborhood wiring contributes into a smart grid to deploy various data services. It is well known that the power grid is one of the most pervasive infrastructure built to provide electricity to customers, therefore, utilizing this infrastructure for digital communications will only result in an ubiquitous telecommunications network. It is common practice to use wires to establish a physical connection in many telecommunications channels, but most electronic devices already have a pair of wires connected to the power lines. Therefore, these wires can be used to simultaneously establish digital communications. Thus, power line communications can be used as an alternative solution to more established technologies such as wireless, coaxial and optical communications. As a promising technology, PLC has attracted a lot of research and has become an active area of research which continues to evolve over time. Notwithstanding its advantages, PLC has issues, namely, severe noise at low frequencies and varying characteristic impedance. This is primarily because the power line channel was not originally designed to be used for communications, thus, it remains a harsh channel. Other challenges arise from the fact that there are different wiring practices around the world, unpredictable loading characteristics as well as differentialand common-mode characteristic impedance. As a result, there is a considerable amount of noise signal attenuation during data transmission. Loss of signal can be addressed by increasing the power at the transmitter, noise reduction and/or reducing channel attenuation to improve the signal-to-noise ratio. However, PLC modems are subject to legislation that impose a limit with regards to the signal levels in the lines. Power lines are good radiators at high frequencies which makes them behave like large antennas with the ability to intercept other radiations in the same frequency range. The radiated signal is proportional to the currents in the line, thus, increasing line currents will not solve the problem but would rather lead to violation of electromagnetic compatibility (EMC) regulations. In this work, an alternative solution is provided which seeks to address the issue of signal attenuation caused by the changing input impedance of a typical power line channel. The deleterious effects of noise are not considered since this work focuses on broadband PLC in the 1–30 MHz frequency range. The objective of this work was to design and build an impedance adaptive coupler to mitigate effects of channel attenuation caused by varying impedance. In this way, the propagating signal will “see” a uniform impedance and as a result the data output will be improved. The work was facilitated by measuring several impedance profiles of PLC channels in the band of interest. Typically, the network topology of PLC networks is not known and the building architectural blueprints are

Power line communication impedance profiling and matching for broadband applications.

This paper models the PLC impulsive noise using a linear superposition of univariate Gaussian distributions where the Bayes’ theorem is used to find the posterior probabilities. The Gaussian mixture is formulated using discrete latent variables and modelled using two, three and four components in order to evaluate the effect of the number of components (Q). The parameters of the Gaussian mixture are then estimated using the maximum likelihood technique and the expectation-maximization algorithm. Regression analysis is proposed in order to solve the issue of singularity which is often present when the maximum likelihood approach is employed. The model is then validated through measurements where the impulsive noise is categorized into low, medium and highly impulsive depending on the amplitude of the indoor PLC noise. It is observed that as the number of components increases the performance of the Gaussian mixture model also increases as depicted by the correlation coefficient and RMSE. The χ2 test indicates that the proposed model provides a better fit as the PLC noise amplitude increases. In addition, the shape of the impulsive noise PDF becomes more defined with higher Q values. A singularity case is also examined where the Gaussian mixture model also provides a good approximation of the measured data. 

Low-Voltage PLC Noise Modelling

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Papers

Power line communication impedance profiling and matching for broadband applications.

Low-Voltage PLC Noise Modelling

Variational Bayesian Learning for the Modelling of Indoor Broadband Powerline Communication Impulsive Noise

K-Means Initialisation for the Modelling of PLC Noise in Indoor Environment