This paper presents the detection of High Impedance Fault (HIF) in solar Photovoltaic (PV) integrated power system using recurrent neural network-based Long Short-Term Memory (LSTM) approach. For study this, an IEEE 13-bus system was modeled in MATLAB/Simulink environment to integrate 300 kW solar PV systems for analysis. Initially, the three-phase current signal during non-faulty (regular operation, capacitor switching, load switching, transformer inrush current) and faulty (HIF, symmetrical and unsymmetrical fault) conditions were used for extraction of features. The signal processing technique of Discrete Wavelet Transform with db4 mother wavelet was applied to extract each phase's energy value features for training and testing the classifiers. The proposed LSTM classifier gives the overall classification accuracy of 91.21% with a success rate of 92.42 % in identifying HIF in PV integrated power network. The prediction results obtained from the proffered method are compared with other well-known classifiers of K-Nearest neighbor's network, Support vector machine, J48 based decision tree, and Naïve Bayes approach. Further, the classifier's robustness is validated by evaluating the performance indices (PI) of kappa statistic, precision, recall, and F-measure. The results obtained reveal that the proposed LSTM network significantly outperforms all PI compared to other techniques.

The electric sector is majorly concerned about the greenhouse and non-greenhouse gas emissions generated from both conventional and renewable energy sources, as this is becoming a major issue globally. Thus, the utilities must adhere to certain environmental guidelines for sustainable power generation. Therefore, this paper presents a novel nature-inspired and population-based Harris Hawks Optimization (HHO) methodology for controlling the emissions from thermal generating sources by solving single and multi-objective Optimal Power Flow (OPF) problems. The OPF is a non-linear, non-convex, constrained optimization problem that primarily aims to minimize the fitness function by satisfying the equality and inequality constraints of the system. The cooperative behavior and dynamic chasing patterns of hawks to pounce on escaping prey is modeled mathematically to minimize the objective function. In this paper, fuel cost, real power loss and environment emissions are regarded as single and multi-objective functions for optimal adjustments of power system control variables. The different conflicting framed multi-objective functions have been solved using weighted sums using a no-preference method. The presented method is coded using MATLAB software and an IEEE (Institute of Electrical and Electronics Engineers) 30-bus. The system was used to demonstrate the effectiveness of selective objectives. The obtained results are compared with the other Artificial Intelligence (AI) techniques such as the Whale Optimization Algorithm (WOA), the Salp Swarm Algorithm (SSA), Moth Flame (MF) and Glow Warm Optimization (GWO). Additionally, the study on placement of Distributed Generation (DG) reveals that the system losses and emissions are reduced by an amount of 9.8355% and 26.2%, respectively.

This study presents a nature-inspired, and metaheuristic-based Marine predator algorithm (MPA) for solving the optimal power flow (OPF) problem. The significant insight of MPA is the widespread foraging strategy called the Levy walk and Brownian movements in ocean predators, including the optimal encounter rate policy in biological interaction among predators and prey which make the method to solve the real-world engineering problems of OPF. The OPF problem has been extensively used in power system operation, planning, and management over a long time. In this work, the MPA is analyzed to solve the single-objective OPF problem considering the fuel cost, real and reactive power loss, voltage deviation, and voltage stability enhancement index as objective functions. The proposed method is tested on IEEE 30-bus test system and the obtained results by the proposed method are compared with recent literature studies. The acquired results demonstrate that the proposed method is quite competitive among the nature-inspired optimization techniques reported in the literature.

A novel nature inspired population-based Harris Hawk Optimization (HHO) method for solving optimal power flow (OPF) problem is proposed in this paper. OPF is a non-linear, highly coupled non-convex constrained optimization problem that minimizes the objective function considering both equality and inequality constraints of the system. In this work, HHO is employed as the main optimizer to adjust the control variables namely real power dispatch by minimizing the fitness function. The proffered method is a single objective approach that aims to minimize the selective objective such as fuel cost or losses of the system by meeting the growing electric demands. Thus, the dynamic nature of chasing pattern of hawks and escaping pattern of prey, results in the global optimal solution. The proposed method is coded using MATLAB software and the effectiveness of method is tested in standard IEEE-30 bus system. The results obtained under selective objective shows that the fuel cost and losses of the system are minimized compared to other well-known intelligence algorithms such as Bat, Butter-Fly (BF), Ant-Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), Moth Flame (MF) and Glow Warm Optimization (GWO). It is inferred that the proposed method outperforms in terms of global search ability and convergence property.

In this study, a nature inspired, intelligence swarm-motivated Salp Swarm Algorithm (SSA) has been used for solving non-linear, constrained and non-convex based optimal power flow (OPF) problem. The sailing and exploring characteristics of Salp in the seas exhibit swarming features that helps in finding the optimal solution. The prime objective of this paper is to optimize the generation cost and power loss by the proposed method while satisfying both equality and inequality constraints of the power system. The proffered method is a single objective approach that aims to minimize the selective objective such as fuel cost or losses of the system by meeting the load demand. In this work, the effectiveness of the Salp Swarm is employed as the main optimizer to adjust the control variables such as real power dispatch by optimizing the fitness function. Thus, the distinguished nature of swarming pattern of Salp and their exploring behavior, demonstrates the global optimal solution. The proffered method is coded using MATLAB software and its performance is validated for IEEE-30 bus system. The results accumulated under selective objective shows that the fuel cost and losses of the system are minimized compared to other well-known computational intelligence (CI) algorithms presented. Moreover, the proposed method reveals a substantial improvement in terms of global search capability and convergence attribute.