Photovoltaic Models Parameters Estimation Based on Elephant Herding Optimization Algorithm

A considerable number of intermittent renewable resources like photovoltaic generation and wind energy when integrated into the conventional grid technology causes serious issues in the power systems like frequency instability. So an intelligent controller is desired for steady and reliable operation of the electric grid. This paper deals with the frequency control of hybrid power system (HPS) employing a sine cosine adopted Harris' hawks optimization (SCaHHO) technique. The proposed technique aims at improving the performance of the original Harris' hawks optimization (HHO) algorithm by incorporating a sine and cosine function in the calculation of escaping energy and chances of escaping of prey respectively to emphasize the exploitation process of hawks. The effectiveness of the SCaHHO technique is validated with the novel HHO technique as well as moth flame optimization, sine cosine algorithm, grey wolf optimization, salp swarm algorithm and gravitational search algorithm using 23 standard test functions. A non-parametric statistical investigation is also carried out to verify the effectiveness of the suggested technique. Later the SCaHHO technique is utilized to tune a newly proposed adaptive fuzzy proportional integral derivative controller (AFPID) for frequency control of HPS. The suggested SCaHHO-tuned AFPID controller achieves an improved control action by moderating the frequency fluctuations as compared to the PID controller. Hardware-in-loop validation of the proposed load frequency control scheme is also carried out using OPAL-RT to measure its fidelity with the numerical simulation results.

The elephant herding optimization (EHO) algorithm is a relatively novel population-based optimization technique, which mimics herding behavior and can be modeled into two operators: clan updating operators and separating operators. Also, in the literature, EHO has received a great deal of attention from researchers since it was proposed applied to many application fields for its advantages of excellent global optimization ability and ease of implementation. However, there is still an insufficiency in the EHO algorithm regarding its lack of exploitation, which leads to slow convergence. In this paper, we propose three enhanced versions of EHO based on the γ value termed EEHO15, EEHO20, and EEHO25 to overcome the problems of fast unjustified convergence toward the origin of the basic EHO. The exploration/exploitation abilities of the EEHO algorithms are achieved by the updating of the two operators (clan and separation operator). To tackle this drawback, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms in general. Moreover, we utilize CEC'17 test suite benchmark functions to test the performance of the proposed three versions of EEHO against EHO, particle swarm optimization (PSO), bird swarm algorithm (BSA), and ant lion optimizer (ALO) algorithms. Eventually, the experimental results revealed that the proposed EEHO algorithms extremely obtained better results compared with other competitive algorithms.

The K-Medoids algorithm is adopted along with the proposed elephant herd optimization (EHO) algorithm in this peculiar work to cluster the consumers according to their required load consumption patterns and for price-based demand response (DR) program implementation. Consumer baseline load (CBL) estimation is done using the clustered load data. The novel approach of clustering the data analysis in this proposed work is done by collecting the load data from the transformer centers under the control of the 33/11 kV substation in the state of Goa, India, as well as the price data from the state load dispatch center and Indian energy exchange. Further the aggregated load data obtained for the 11 kV feeder is used to estimate the reference consumer load consumption by using regression and an artificial neural network model. Consumer response towards load change with price is modeled based on price elasticity and the consumer benefit function. An EHO algorithm is proposed for implementing the time of use (TOU) DR program for domestic consumers. The algorithm’s effectiveness and robustness is demonstrated by comparing the results with those of the genetic algorithm and particle swarm optimization technique. The proposed work of CBL estimation, consumer segregation, and optimum evaluation of the TOU DR program using the EHO algorithm assists in the implementation of DR programs. The proposed research can be used for analyzing the demand reduction potential, deployment of alternative energy sources, reduction in the use of conventional energy sources, promotion of energy efficiency, and the reduction of CO2 emissions.

The invention of Smart Grid (SG) have revolutionized the traditional electricity consumption pattern as well as distribution. The technology of communication and information have been involved in almost every domain so for Smart Grids. Production of electricity is not cheap, hence with the help of information and technology, Smart Meters (SM) play vital role to control, manage and perform optimization to utilize the electric power efficiently on consumer side and called Demand Side Management (DSM). In the proposed research paper, a Home Energy Management System (HEMS) have been proposed to optimize the home appliances to reduce the maximum cost. Elephant Herd Optimization (EHO) algorithm have been implemented along with our own hybrid of EHO algorithm. This EHO is hybrid of Genetic Algorithm (GA) and called Genetic Elephant Herd Optimization (GEHO). Results of simulation shows that GEHO scheduled the appliance more efficiently to reduce maximum cost when comparing with regular EHO and unscheduled schemes. Peak to Average Ration (PAR) also have been observed. GEHO and unscheduled have equal PAR due to scheduling of maximum appliances on either sides but EHO have small PAR. For further understanding of cost optimization different Operation Time Interval (OTI) have been applied. Trends of load and cost with all of three schemes have been discussed in detail.

In order to improve the accuracy of network intrusion detection, it is necessary to optimize Support Vector Machine (SVM) parameters. In view of the advantages of Elephant Herding Optimization (EHO) algorithm, such as simple control parameters and easy combination with other algorithms, this paper tries to optimize these parameters by using an Improved EHO (IEHO) algorithm. The IEHO-SVM algorithm is then proposed for parameters optimization, in order to improve the accuracy of network intrusion detection. The simulation experiment uses the KDD CUP99 data set for verification analysis. The experimental results show that, compared with the Particle Swarm Optimization (PSO)-SVM algorithm, Month-flame Optimization (MFO)-SVM algorithm and the basic EHO-SVM algorithm, the IEHO-SVM algorithm not only improves the global search ability of network intrusion, but also increases the accuracy rate of network intrusion detection by an average of 7.36%, 4.23% and 5.56% respectively, and reduces the false alarm rate by an average of 3.04%, 2.41% and 3.07% respectively, which aims at improving the efficiency of network intrusion detection.

In evaluating solar photovoltaic (SPV) cell performance and monitoring operational deviations, parameters of the solar cell equivalent circuit models play an important role. Given that solar cells have nonlinear current-voltage characteristics, calculating their parameters is a significant challenge. Therefore, to effectively handle this engineering challenge, an accurate and efficient optimization technique is typically needed. In order to determine SPV cell parameters, this study revealed a new optimization technique called Ali Baba and the Forty Thieves (AFT) algorithm. The suggested optimization technique was used to estimate the parameters of the single-diode equivalent circuit model of the SPV. Study work has been done on various photovoltaic modules, including the Photowatt PWP-201 and the RTC France solar cell. In addition, a comparison study is used to demonstrate the proposed AFT algorithm's performance against a number of existing heuristic algorithms, including the Moth Flame Optimization (MFO), Dragonfly Algorithm (DA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Ant Lion Optimization (ALO), Harris Hawk Optimization (HHO), Hybrid of Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), Marine Predator Algorithm (MPA), and African Vulture Optimization Algorithm (AVOA). The comparison has been made using the same datasets and the same computational workload for each optimization technique in order to evaluate performance fairly. The acquired comparative results demonstrated that the AFT algorithm provides higher performance than any of those techniques in terms of the root mean square error (RMSE), computation time, and the accuracy of the results for parameter estimation of SPV cells.

The rapid growth in wireless technology is enabling a variety of advances in wireless sensor networks (WSNs). By providing the sensing capabilities and efficient wireless communication, WSNs are becoming important factor in day to day life. WSNs have many commercial, industrial and telecommunication applications. Maximizing network lifespan is a primary objective in wireless sensor networks as the sensor nodes are powered by a non-rechargeable battery. The main challenges in wireless sensor networks (WSNs) are area of coverage, network’s lifetime and aggregating. Balanced node establishment (clustering) is the foremost strategy for extending the entire network's lifetime by aggregating the sensed information at the head of the cluster. The recent research trend suggests Meta-heuristic algorithms for the intelligent selection of ideal Cluster Heads (CHs). The existing Cluster Head Selection (CHS) algorithm suffers from the inconsistent trade-offs between exploration – exploitation and global best examine constraints. In this research, a novel Camel series Elephant Herding Optimization (CSEHO) algorithm is proposed to enhance the random occurrences of Camel algorithm by the Elephant Herding Optimization algorithm for optimal CHS. The Camel algorithm imitates the itinerant actions of a camel in the desert for the scavenging procedure. The visibility monitoring condition of the camel algorithm improves the efficiency of exploitation, whereas the exploration inefficiency of a Camel algorithm is compensated optimally by the Elephant Herding Optimization operator (Clan and separator). The superior performance of the proposed CSEHO algorithm is validated by comparing its performance with various other existing CHS algorithms. The overall attainment of the offered CSEHO algorithm is 21.01%, 31.21%, 44.08%, 67.51%, and 85.66%, better than that of EHO, CA, PSO, LEACH, and DT, respectively.

Snake Optimizer: A novel meta-heuristic optimization algorithm

A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters

SummaryDetecting hiding communities is considered as a main topic in complex networks. In this article, we propose a multi‐swarm elephant herding optimization (EHO) algorithm to uncover community structures in complex environments. It adapts EHO algorithm to community detection problem. EHO algorithm relies on two procedures which are updating clan procedure and separating procedure. The main idea of our multi‐swarm approach is that the population is composed of a set of interacting clans. In each clan, a local search function is defined to determine best local individual called matriarch. Through updating clan procedure, the remaining individuals in the clan update their positions based on the matriarch position. In addition, to ensure significant individuals in the clan, a multi‐swarm cooperative algorithm is designed to implement separating procedure; clans interchange individuals to balance the exploration and exploitation abilities. A series of experiments are carried out on artificial and real networks. The results obtained by the proposed approach are better than the results obtained by some other approaches.

An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection

Dynamic Harris hawks optimizer based on historical information and tournament strategy and its application in numerical optimization of blast furnace ingredients

This work portrays control of frequency in an unique independent double area interconnected hybrid microgrid system (IHM) including a novel combined solar gas turbine (CSGT), biodiesel generator (BDG), wind turbine generator (WTG), energy storage units and DC link (DCL) using tilt‐integral‐derivative (TID) controller. A recently developed metaheuristic algorithm termed as Harris's hawk optimization (HHO) technique is used to obtain the controller gain constraints. Use of CSGT in 2‐ area IHM for load frequency control (LFC) is a novel work and use of TID controller and HHO technique for tuning the controllers' parameters for such IHM are the other unique works. The dynamic performance of TID, PID and PI controllers are compared with the gains obtained by tuning with different algorithms. The results obtained established the superiority of HHO optimised TID controller. The effect of each component on the system are analysed subjected to consideration of different real‐time scenarios. At last, a sensitivity analysis is performed by varying the source and load conditions to establish the dominance of the HHO optimised TID controller.

Electricity bidding plays a key role in a restructured electricity market. This paper proposed an optimal method to minimize the system generation cost of active power produced by thermal generators in a photovoltaic (PV) integrated deregulated power market with considering demand side bidding. The proposed problem has been solved in two folds: initially PV power generator is optimally placed in the system based on the objective of minimum fuel cost and minimum system losses. Secondly one load bus has been selected for demand side bidding based on the demand of the buses and system requirements. To solve and validate this problem, Whale Optimization Algorithm (WOA) has been used in this work. To validate and compare the obtained results, Elephant Herding Optimization (EHO) Algorithm and Artificial Bee Colony (ABC) algorithm are used in this paper. The usefulness of these techniques is applied in IEEE 30 bus system for the analysis purpose.

MULTIPLE SECRET SHARE CREATION SCHEME WITH ELEPHANT HERD OPTIMIZATION ALGORITHM FOR BIOMETRIC IMAGE SECURITY