Because of dust, trees, high buildings in the surrounding area, partial shading conditions (PSC) occur in photovoltaic (PV) systems. This condition affects the power output of the PV system. Under PSC there is a global maximum power point (GMPP) besides there are a few local maximum power points (LMPP). This condition makes the maximum power point tracking (MPPT) procedure a challenging task. In order to solve this issue, soft computing techniques such as gray wolf optimization (GWO), particle swarm optimization (PSO) and Gravitational Search Algorithm (GSA) are implemented. However, the performance of MPP trackers still needs to be improved. The main contribution of this paper is improving the tracking speed by implementing BOA to the MPPT of the PV system under PSC. Thus, in real-time applications a promising alternative presented to the literature to improve the performance of the PV systems under variable PSC because of its fast tracking speed. PV system consists of PV array, boost converter and load are modeled and simulated in MATLAB/Simulink. BOA algorithm is implemented for three different insolation scenarios on the PV array. The results of the BOA algorithm verified by a comparative analysis with PSO-GSA and GWO algorithms. The results show that BOA can give high accuracy and better tracking speed than these algorithms in recent literature.

Hybrid active power filter (HAPF) consisting of passive filter and active filter in various configurations to each other has now become preferred technology for harmonic compensation in two wire, three wire and four wire ac power networks with nonlinear loads. This paper presents a detailed survey of hybrid active power filters considering converter topologies, supply system and passive filter type. In addition, the control strategies are discussed in detail. The main aim of this paper is to provide a broad perspective on the status of HAPF technology to researchers and application engineers dealing with power quality. More than eighty research papers are reviewed and classified into categories and subcategories.

The rapid development of digital technologies and solutions is disrupting the energy sector. In this regard, digitalization is a facilitator and enabler for integrating renewable energies, management and operation. Among these, advanced monitoring techniques and artificial intelligence may be applied in solar PV plants to improve their operation and efficiency and detect potential malfunctions at an early stage. This paper proposes a Digital Twin DT concept, mainly focused on O&M, to obtain more information about the system by using several artificial intelligence boxes. Furthermore, it includes the development of several machine learning (ML) algorithms capable of reproducing the expected behavior of the solar PV plant and detecting the malfunctioning of different components. In this regard, this allows for reducing downtime and optimizing asset management. In this paper, different ML techniques are used and compared to optimize the selected methods for enhanced response. The paper presents all stages of the developed Digital Twin, including ML model development with an accuracy of 98.3% of the whole DT, and finally, a communication and visualization platform. The different responses and comparisons have been made using a model based on MATLAB/Simulink using different cases and system conditions.