In this paper, efficient and accurate linear and nonlinear models are proposed for indicating comprehensive health requirements of the transformer using health index (HI) concept. The models are established with 336 experimental datasets including oil characteristics and dissolved gas analysis (DGA) of various types of transformers placed in different areas. The significance of DGA parameters in transformer health condition is considered with the inclusive DGA factor ( DGAF ) parameter, which considers the weighting importance of seven dissolved gases. Nonlinear models used in this paper are artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), which represent the behavior of transformer insulation parameters. The nonlinear models are compared with multiple linear regression (MLR) which is a linear statistical model. The models are established with 80 percent of the experimental dataset. The other 20 percent of data are utilized for the efficiency assessment of the models. The results demonstrate that the models provide an assessment of the health condition of the transformers comparable to existing models with high accuracy. The contributions of this paper are: 1) Evaluating the overall HI of the transformer employing a complete set of 15 input parameters of transformer oil-paper insulation system. 2) Adding DGAF , %WaterPaper , IFT parameters and showing the importance of these parameters. 3) Regarding the condition of solid insulation of the transformer particularly. 4) Applying a diverse and large practical dataset composed of 336 different transformers located in different country areas. 5) Using the MLR method for three purposes. 6) Providing linear (MLR) and nonlinear (ANN, ANFIS) models for HI calculation of the dataset, simultaneously. 7) Verifying the applicability and efficiency of the ANFIS model for simulating HI value.

The study proposes a modified version of African Vultures Optimization Algorithm (AVOA) to address the optimal power flow (OPF) issue. The developed optimizer is called Chaotic AVOA (CAVOA). Prior to applying CAVOA to the problem at hand, its accuracy is evaluated using 23 benchmark functions. The production powers of WTs that are interrupted are predicted using Weibull probability density functions (PDFs). Two additional costs, namely penalty cost and reserve cost, are incorporated into the goal function of the OPF. The simulation results of CAVOA are compared with those of the original AVOA to solve the OPF. The proposed OPF technique and its solution methodology are verified on the IEEE 30‐bus test system, taking into account the flexible alternating current transmission systems (FACTS) devices, which have several benefits such as reducing active power transmission loss, regulating power flow, and improving voltage stability/profile. The research's simulation results demonstrate that CAVOA is more effective in identifying the OPF's optimal solution by reducing the overall power cost and power losses. The results indicate that the CAVOA algorithm outperforms AVOA as a metaheuristic optimization algorithm due to its superior capability in addressing challenging OPF problems with a minimal convergence rate. For example, when comparing the two algorithms, CAVOA achieved a significant improvement. It successfully reduced the cost function by approximately 0.023% in Case I and 0.035% in Case II, while also decreasing power loss by 2.57% in Case II and 0.57% in Case I. Additionally, CAVOA exhibited a remarkable reduction of 5.36% in voltage deviation compared to AVOA in Case I. Furthermore, in Case III, CAVOA demonstrated a decrease of 0.64% in gross cost when compared to AVOA.

Agrovoltaics, also known as Agri‐PV or AV, is an innovative approach that entails the shared utilization of land for both the production of agricultural commodities and energy generation. This concept has gained immense popularity in recent times owing to its ability to boost income per unit of land area significantly. The scope of AV systems is quite extensive, as it encompasses solar energy converters and other renewable energy sources like bioenergy. Current strategies for agrovoltaic (AV) in agriculture are the outcome of the gradual development of agroecology and the integration of photovoltaic (PV) power supply into the grid. These approaches could lead to a nearly doubled income per unit area. Without on‐site power supply, reduced chemical fertilizers and pesticides, and on‐site yield processing, AV has the potential to revolutionize large‐scale unmanned precision agriculture and smart farming. These approaches might lead to significant changes in the logistics and value‐added production chain, thereby reducing agriculture's carbon footprint. In the future, it is possible to reduce the cost of AV technology by half by utilizing decommissioned solar panels in the technology and to delay the need for bulk PV recycling by several years. This review presents a different perspective to the common discourse on the topic, by giving special emphasis to the potential to further integrate AV into agriculture, which has the potential to facilitate the resolution of relevant legal disputes over the use of AV.

The importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm “LWaOA,” making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA.

Wind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card.