Aiming at the new risk of power data loss brought by the introduction of new technology into traditional power grid by Advanced metering infrastructure(AMI), an adaptive method for detecting abnormal data of smart meters based on Gaussian mixture model linear discriminant aualysis (GMM-LDA) clustering feature learning and particle swarm optimization support Vector Machine (PSO-SVM) is proposed. In order to detect abnormal electricity consumption behavior in AMI. Firstly, GMM-LDA clustering feature learning algorithm is used to cluster some data sets to obtain the optimal feature representations of normal pattern feature database and abnormal pattern database. Then, an adaptive updating mechanism of pattern database is introduced to make the abnormal detection model adapt to the dynamic changes of network environment, and the labeled data set is compiled. The training samples are sent to the PSO-SVM classifier for learning. Finally, the unknown label data sets are classified, and the abnormal electricity data can be traced back to the suspected users, so that realize the key monitoring of the suspected users. Experiments on existing data sets show that the proposed detection model not only avoids the dependence on manual classification in supervised training samples, but also has higher detection accuracy and lower false alarm rate than single or other algorithms.

Due to the widespread integration of renewable energy sources connected to AC and DC power systems by means of power electronics converters, electromagnetic noise propagates along lines, and metallic earth-return structures. EMI filters are commonly used to mitigate the common mode and differential mode noise at the interface of distribution lines, and their suppression characteristics are usually assessed in standard test setups, the impedances of which are, however, scarcely representative of real-world applications. In this paper, an online, inductively coupled impedance measurement method is proposed. A sensitivity analysis to highlight the benefits of the proposed setup and experimental verification is performed. The proposed method enables non-intrusive impedance measurement in energized systems, including power converters. These measures, in turn, allow the evaluation of modal insertion losses of EMI filters in real-world operating conditions. The three-phase example considered in this study shows significant deviations from manufacturer specifications, thus justifying the need for more advanced estimation techniques.

Electromagnetic interference (EMI) from renewable power systems to the grid attracts more attention especially in the low-frequency range, due to the low switching frequency of high-power inverters. It is significantly important to derive EMI models of power inverters as well as to develop strategies to suppress the related conducted emissions. In this work, black-box modelling is applied to a three-phase inverter system, by implementing an alternative procedure to identify the parameters describing the active part of the model. Besides, two limitations of black-box modelling are investigated. The first regards the need for the system to satisfy the linear and time-invariant (LTI) assumption. The influence of this assumption on prediction accuracy is analysed with reference to the zero, positive and negative sequence decomposition. It is showing that predictions for the positive/negative sequence are highly influenced by this assumption, unlike those for the zero sequence. The second limitation is related to the possible variation of the mains impedance which is not satisfactorily stabilized at a low frequency outside the operating frequency range of standard line impedance stabilization networks.

Random Pulse Width Modulation (RPWM) allows controlling the switching signal of power converters in order to reduce the harmonic peaks by spreading the noise spectrum. Currently, many manufacturers of power converters are deploying this modulation scheme in order to comply with Electromagnetic Compatibility (EMC) test requirements. However, when the converters coexist with Power Line Communication (PLC) systems, such as in Smart Grid (SG) applications, resorting to RPWM needs further investigations since it potentially affects the communication channel by increasing the bit error rate. This possible detrimental effect is investigated in this work, by considering a PLC system for automatic meter reading (AMR) implemented in a SG application. To this end, the model of a complete PLC system is implemented in SIMULINK, and Quadrature Phase Shift Keying (QPSK) modulation is used to model the PLC modems in the communication channel. Results show that, even if the deployment of RPWM techniques may lead to an appreciable reduction/spreading of the peaks in the noise spectrum, it may also lead to an increase of the bit error rate on the PLC system.

Power Line Communication (PLC) technologies are being used in many applications and offer the advantage of utilizing existing power cables for both power and data transmission, thus minimizing cost and complexity. Nevertheless, PLC technology requires further investigation to solve possible co-existence issues. Indeed, recent studies confirmed that alternative modulation schemes such as Random Pulse Width Modulation (RPWM), applied to switching-mode power converters to minimize conducted emissions, detrimentally interfere with the PLC system. This paper presents an experimental test campaign aimed at investigating the effects of RPWM on the G3-PLC system, with the final goal of understanding the conditions under which RPWM schemes can be considered as an effective alternative to conventional Pulse Width Modulation (PWM) in applications involving PLC systems. In details, the effects of different RPWM parameters such as switching frequency, modulation index, and Random Number Update Rate (RNUR) on the G3-PLC is investigated. In addition, different RPWM schemes such as Random Frequency Modulation (RFM) and Random Pulse Position Modulation (RPPM) are compared in terms of performance so as to highlight which RPWM is best suited to assure coexistence with PLC systems. The impact of RPWM on the communication channel is evaluated in terms of Frame Error Rate (FER), Channel Capacity, and Channel Capacity Loss metrics. Experimental results confirmed that randomly modulated converters with switching frequencies near the G3-PLC bandwidth cause more significant disturbance and possible coexistence issues than the switching frequencies out of this range. Results also show that the modulation index and the RNUR of RPWM have a direct effect on the communication channel. Moreover, a trade-off between Electromagnetic Interference (EMI) reduction and coexistence issues is observed: RFM, which is very effective for EMI reduction, is found to be very disruptive for G3-PLC, compared to alternative random modulation techniques such as RPPM.

The spread of information-communication technology (ICT) devices in full and/or hybrid electric vehicles is posing increasing issues about coexistence between communication and electric power systems. Particularly, under different operating conditions, the time-variant and nonlinear behavior of the involved power-electronic devices may cause detrimental electromagnetic interference (EMI) effects for onboard ICT devices. The use of passive or active EMI filters can mitigate EMI, yet their effective design requires the knowledge of the actual modal impedances of the involved components. With the final objective to support EMI filter design, in this work, the frequency response of the common mode (CM) and differential mode (DM) impedances of a complete automotive power train is investigated under different operating conditions. Moreover, both low-frequency functional aspects and high-frequency parasitic effects are considered and modelled properly.