Power quality issues are required to be addressed properly in forthcoming era of smart meters, smart grids and increase in renewable energy integration. In this paper, deep auto-encoder (DAE) networks is proposed for power quality disturbance (PQD) classification and its location detection without using complex signal processing techniques and complex classifiers. In this technique, Gabor filter is used to extract a set of general Gabor features from the convolution of PQD image. Subsequently, through sparse based DAE network, essential and optimal features are extracted and learnt which are used by a simple classifier (SoftMax) to classify the PQD type. Furthermore, the temporal information of the PQD is obtained using the PQD image to correctly locate the disturbance’s initiating and terminating instants. The proposed DAE network has the benefits of deep learning-based networks in terms of automatic feature selection, but it requires smaller data sets. The issue of obtaining optimised, robust, and strong features from the PQD signal is thus resolved. Excellent classification accuracy of PQD is obtained with appropriate network parameter setting of the proposed DAE network. The proposed technique is compared with three other methods support vector machine (SVM), stacked auto-encoder (SAE) and principal component analysis (PCA) for PQD classification by implementing all the four techniques on python platform using the same data set. It has an overall classification accuracy of more than 97% at a signal to noise ratio (SNR) of 20dB, which is on the higher side when compared to other methods of PQD detection under noisy environment. Additionally, this method requires less computation time with the same data set than alternative approaches like SVM. Thus, the proposed method outperforms existing methods for PQD classification and detection of single disturbance and multi-disturbance in terms of greater accuracy and reduced computation complexity and computation time.

This paper focuses on the control development of a grid-tied photovoltaic system coupled with a shunt active power filter. The considered power system includes a PV panel, a single-capacitor three half-bridge interleaved buck converter connected to a three-phase power grid and a nonlinear load. This study aims to accomplish simultaneously three objectives: i) to ensure a high compensation level of current harmonics and reactive power consumed by the nonlinear load in order to perform power factor correction at the grid side; ii) to extract the maximum power from the PV panel regardless the climatic conditions; iii) to regulate the dc bus capacitor voltage to its reference provided by the MPPT controller. To this end, a controller using a two-loop cascade strategy is designed. First, the inner loop is developed with the hybrid automaton approach to formulate the switching control signals with the aim of guaranteeing a unitary power factor. Second, the outer loop is designed using fuzzy logic control to ensure the regulation of the photovoltaic voltage, while the perturb and observe algorithm is employed to perform maximum power point tracking. Finally, the system with the developed control scheme is simulated using MATLAB/SimPowerSystems environment. The obtained results confirm that the proposed controller is able to achieve the previous objectives under varying environmental conditions.

This article focuses on analyzing the fast-scale stability in a peak current-mode-controlled boost converter with a constant power load (CPL). Since with this kind of nonlinear loads the analytical expression of the discrete-time model is not available and the existing averaged models are inadequate in describing accurately the fast-scale dynamics of these converters, a piecewise linear switched model is established by approximating the CPL by a composite linear load. The model is first demonstrated to predict accurately the same bifurcation points predicted by the switched model using the nonlinear load. The bifurcation analysis using the Floquet theory and the resulting monodromy matrix and its eigenvalues loci clearly show the effect of system parameters on its stability. The fast-scale stability boundaries in terms of suitable parameters are obtained by using eigenvalues analysis. The effect of the proportional gain of the voltage controller is studied. Finally, experimental results are further given to verify the theoretical analysis and simulation results obtaining a good agreement.

In the era of pervasive digital connectivity, intelligent surveillance systems (ISS) have become essential tools for ensuring public safety, protecting critical infrastructure, and deterring security threats in various environments. The current state of these systems heavily relies on the computational capabilities of mobile devices for tasks such as real-time video analysis, object detection, and tracking. However, the limited processing power and energy constraints of these devices hinder their ability to perform these tasks efficiently and effectively. The dynamic nature of the surveillance environment also adds complexity to the task-offloading process. To address this issue, mobile edge computing (MEC) comes into play by offering edge servers with higher computational capabilities and proximity to mobile devices. It enables ISS by offloading computationally intensive tasks from resource-constrained mobile devices to nearby MEC servers. Therefore, in this paper, we propose and implement an energy-efficient and cost-effective task-offloading framework in the MEC environment. The amalgamation of binary and partial task-offloading strategies is used to achieve a cost-effective and energy-efficient system. We also compare the proposed framework in MEC with mobile cloud computing (MCC) environments. The proposed framework addresses the challenge of achieving energy-efficient and cost-effective solutions in the context of MEC for ISS. The iFogSim simulator is used for implementation and simulation purposes. The simulation results show that the proposed framework reduces latency, cost, execution time, network usage, and energy consumption.