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With the frequent occurrence of extreme weather, power transmission line icing, a general phenomenon during winter which may bring serious economic losses to the electric power system, has attracted increasing attention. However, owing to the complexity nature of wire ice covering, it is essential to establish an icing thickness forecasting model with high-accuracy for guaranteeing the security and stability of the power grid. Hence, this paper proposes a hybrid model that combines wavelet transform (WT) with extreme learning machine optimized by bat algorithm (BA-ELM). The original icing data containing icing thickness and meteorological factors are first denoised by WT and then divided into several stages based on the characteristics of icing period. Bivariate correlation analysis and partial auto correlation function (PACF) are used to select the inputs of different stages. Subsequently, ELM whose input weights and bias threshold were optimized BA is built to forecast icing thickness. To verify the developed model, icing data from two power transmission lines located in Hunan province are applied for experiments. The simulation results demonstrate that not only the proposed model shows a better performance but also the staged modeling can highly improve icing thickness prediction accuracy.

Solar energy has been widely adopted in power systems, particularly using the photovoltaic (PV) generation technology. In this respect, the power generation of such a technology is highly impacted by several factors, such as temperature and solar irradiance. As an effective solution, maximum power point tracking (MPPT) approaches have been developed and used in PV systems to increase the efficiency subject to the changing climate conditions. In this respect, a combinatorial MPPT technique is presented in this paper based on the fuzzy controller and BAT optimization algorithm to desirably tune the control parameters. To this end, the membership functions of the fuzzy logic controller (FLC) are appropriately specified to cope with uncertainties caused by changing climatic conditions. The studied PV system, equipped with the MPPT technique, operates jointly with an electrical energy storage system which is based on a lead-acid battery. By employing this hybrid generation system, the solar power generation intermittency can be well compensated and the stabilized power output can be achieved. The proposed model is then simulated on a typical hybrid energy system, including a PV system and a battery energy storage (BES) system. In this respect, the superior performance of the suggested control scheme is verified through making a comprehensive comparison with other well-known techniques. Besides, the behavior of the system under varying climate conditions is studied and the desired performance of the suggested combinatorial controller is validated. For example, the presented BAT-FLC scheme can help the hybrid system reach 99% efficiency for partial shading conditions (PSCs) which is 18% more compared to the prevalent perturb and observe (P&O) technique.

In order to increase the production of photovoltaic-based cleaner energy, the maximum power point tracking (MPPT) controller was employed. Because different MPPT algorithms have been adduced in the literature, it is mandatory to compare and differentiate them to select the most adequate MPPT for a particular application or to make suggestions for future research regarding MPPT. This paper reviews (extensively) the most used MPPT algorithms. They are classified into three groups: (1) direct, such as hill climbing (HC), perturb and observe (PO (2) indirect, namely fractional short-circuit current (FSCC), Fractional Open-Circuit Voltage (FOCV), and pilot cell and (3); soft computing methods such as a Kalman filter, fuzzy logic control (FLC), neural network, partial swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), bat algorithm and hybrid PSO-FLC. A detailed principle of operation and the merits and downsides of each method are discussed. Also, the distributed MPPT and other MPPT methods are briefly presented. Finally, a recommendation of the most well-suited embedded board for the implementation of each method is presented and discussed. To complete these objectives, this manuscript focuses solely on the low-cost embedded board.