Analysis of large scale distribution network using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

This study presents a pelican optimization algorithm (POA) based technique for optimizing the integration of capacitor banks in radial power distribution systems. The proposed approach adopts a hybrid approach that combines the loss sensitivity factors (LSF) and the POA to determine the appropriate placement and sizing of the capacitor banks, respectively, for best system performance. The proposed methodology is tested on the IEEE 33-bus distribution system using MATLAB software. The performance for integrating two capacitor banks is compared to that of the particle swarm optimization (PSO) and the genetic algorithm (GA) methods. The proposed POA method achieved a significant improvement in system performance. Specifically, it reduced active power losses (PLoss) by 68.08%, which surpasses the reductions achieved by PSO (61.79%) and GA (57.73%). In the case of reactive power loss (QLoss), the POA-based approach also proved superior with a 39.035% reduction, while PSO and GA-based methods achieved 28.50% and 22.59% reductions, respectively. These results underscore the effectiveness of the POA-based approach in capacitor bank placement and sizing, making it a promising candidate for enhancing voltage profiles and minimizing power losses in radial distribution systems.

In this paper, proposed optimization technique called whale optimization algorithm (WOA) is presents to find the optimum allocation of distributed generation (DG) and capacitor in radial distribution systems during reduction of single and multi-objective function namely, (network power losses, voltage deviation, and total operating cost). The multi objective function is formed by the use of weighted sum method. In this paper, multiple-DG units have been analyzed under two load power factors (i.e., unity and optimal) with and without capacitor (C). WOA technique has been applied to a 33-bus radial distribution system. The performance of the WOA technique is compared with other evolutionary optimization methods under different operating conditions of the system. The impact of integrating the proper size of DG and C at the suitable placement based on proposed algorithm are shown in the simulation results.

Transmission networks face continuous electrical and mechanical stresses due to increasing system challenges and power losses. Transmission networks require special focus and detailed studies each time a load or a generator emerges to the grid. The interline power flow controller (IPFC) is a relatively new scheme that is implemented in the transmission network to improve transmission efficiency, decrease transmission losses, and enhance voltage profile. In this paper, the interline power flow controller’s impact on transmission network performance is investigated as it is implemented within the IEEE 5-bus, 14-bus, and IEEE 57-bus systems. In addition, the whale optimization algorithm (WOA) is used to optimize the interline power flow controller locations within the system to achieve optimal transmission system performance. WOA performance is also compared to genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, and the superiority of the proposed WOA-based control is proved. The robustness of the optimized system against load variations is investigated and the results introduced affirm the capability of the interline power flow controller to enhance transmission network efficiency.

Distributed generation (DG) is integrated in a passive distribution system to reduce power loss, improve voltage profile, and increase power output. To reap the most benefits of the distribution system, the best location and appropriate DG size must be determined. This paper presents a hybrid Grey wolf Optimizer (GWO) and Particle swarm optimization (PSO) approach for determining the best placement and DG size while considering a multi-objective function that includes active and reactive power loss minimization as well as voltage profile enhancement. Dilla distribution system was used as a case study and the weighted technique was used to convert to a single objective function while taking into account multiple constraints such as bus voltage limit, DG output limit, and branch current limit. DG penetration is limited to up 60% of the total active load on the feeder and a forward–backward sweep load flow algorithm was used to generate the load flow solutions. The findings of the study show that combining three PV-DGs (Case 3) is the best way to improve voltage profile and minimize losses. In addition, the proposed hybrid GWO-PSO algorithm performed better compared to the other four algorithms (Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Particle swarm optimization (PSO), and sine cosine algorithm (SCA)) in terms of achieving the best multi-objective function (MOF) outcome.

In this manuscript, the gain of the EDFA-Raman hybrid optical amplifier (HOA) is maximized using a popular nature inspired Whale Optimization Algorithm (WOA). The dominating parameters of HOA, length of Raman amplifier and EDFA and its pump powers are optimized to get the maximum possible gain. Further, the performance of WOA is compared by using other metaheuristic techniques such as Real coded Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, to optimize the same model of HOA. WOA proves to be a better optimization technique as it is able to find maximum gain, have a better convergence curve and box plot performance as compared to the other three techniques. Moreover, the gain flatness and noise figure performance of HOA is compared with EDFA and Raman amplifier.

Abstract. Most computer vision and photogrammetry applications rely on accurately estimating the camera pose, such as visual navigation, motion tracking, stereo photogrammetry, and structure from motion. The Essential matrix is a well-known model in computer vision that provides information about the relative orientation between two images, including the rotation and translation, for calibrated cameras with a known camera matrix. To estimate the Essential matrix, the camera calibration matrices, which include focal length and principal point location must be known, and the estimation process typically requires at least five matching points and the use of robust algorithms, such as RANSAC to fit a model to the data as a robust estimator. From the usually large number of matched points, choosing five points, the Essential matrix can be determined based on a simple solution, which could be good or bad. Obtaining a globally optimal and accurate camera pose estimation, however, requires additional steps, such as using evolutionary algorithms (EA) or swarm algorithms (SA), to prevent getting trapped in local optima by searching for solutions within a potentially huge solution space.This paper aims to introduce an improved method for estimating the Essential matrix using swarm particle algorithms that are known to efficiently solve complex problems. Various optimization techniques, including EAs and SAs, such as Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Improved Gray Wolf Optimization (IGWO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA) and Whale Optimization Algorithm (WOA), are explored to obtain the global minimum of the reprojection error for the five-point Essential matrix estimation based on using symmetric geometric error cost function. The experimental results on a dataset with known camera orientation demonstrate that the IGWO method has achieved the best score compared to other techniques and significantly speeds up the camera pose estimation for larger number of point pairs in contrast to traditional methods that use the collinearity equations in an iterative adjustment.

This paper describes optimum antenna array structure for beamforming in smart antenna technology. Different examples of array structures are analysed by the use of newly developed optimization technique called Whale Optimization Algorithm (WOA). The design objective here has been considered to get the most favourable radiation patterns with sidelobe level (SLL) reduction by optimizing amplitude excitations, inter element spacings and phases of each elements of an array. The numerical and simulation results are compared with that obtained by most popular optimization technique, Particle Swarm Optimization (PSO), Ant Lion Optimization (ALO) and Cat Swarm Optimization (CSO) by building codes using MATLAB. The results show that WOA can exhibit relatively better performance than other algorithms for the antenna array synthesis examples and beamforming applications.

A hybrid modified step Whale Optimization Algorithm with Tabu Search for data clustering

Accurate electricity price prediction is key to the orderly operation of the electricity market. However, the uncertain, stochastic and fluctuant characteristics of electricity pricees make prediction difficult. With the aim of solving this issue, this investigation proposed a multi-stage intelligent model integrating the Beveridge–Nelson decomposition (B-N-D) model, the least square support vector machine (LSSVM), and a nature-inspired optimization model named the whale optimization algorithm (WOA). Firstly, the B-N-D model was utilized to decompose the hourly electricity price time series into determinacy component, periodic trend, and stochastic item. Secondly, the WOA–LSSVM model was proposed to forecast the future hourly data of three components respectively, of which the optimal parameters of LSSVM were determined by using WOA. Finally, the future hourly electricity price data were computed by multiplying the forecasted data of those terms. To verify the validity of the proposed electricity price prediction model in this paper, five comparison approaches based on the B-N-D approach were selected, which are auto-regressive integrated moving average (ARIMA), single LSSVM, LSSVM optimized by the fruit-fly optimization algorithm (FOA), LSSVM optimized by particle swarm optimization (PSO) models, and WOA–LSSVM without B-N-D. By comparatively analyzing the error criteria values of the above models through testing on the objective data of the Pennsylvania–New Jersey–Maryland (PJM) electricity market collected from 11 December 2017 to 18 December 2017, from 15 January 2018 to 22 January 2018, and from 1 February 2018 to 25 February 2018, we conclude that the constructed intelligent model in this paper can greatly enhance the prediction precision of electricity prices.

Biometrics is a technology that identifies or verifies individuals based on unique physical or behavioural traits, offering a reliable form of authentication in sectors like healthcare, law enforcement, and security. Existing gender detection systems using fingerprints face challenges due to poor image quality and complex ridge patterns, while ConvolutionalNeural Networks (CNNs), though promising, are hindered by issues like overfitting, slow convergence, and getting trapped in local minima.Other optimisation algorithms like Genetic Algorithm (GA), Particle Swarm Optimisation (PSO)and Ant Colony Optimisation(ACO)often face issues likepremature convergence,high computational costsand poor global search ability. Whale Optimisation Algorithm (WOA) was chosen for its faster convergence, simplicity, and better balance between exploration and exploitation in optimising CNN parameters.Therefore, this study developed a fingerprint-based gender detection system through the Optimisationof CNN with Whale OptimisationAlgorithm.A dataset of 2,200 gender-labelled fingerprint images (1,320 male and 880 female) was acquired from Kaggle.com. The images underwent preprocessing involving cropping, grayscale conversion, histogram equalization for enhancement, and edge detection filtering to eliminate noise. OptimisedCNN model was formulated using Whale OptimisationAlgorithm (WOA) by tuning CNN hyperparameters: number of neurons and dropout rate. The resulting WOA-CNN was employed for feature extraction (edges, texture patterns, shapes) and detection of fingerprint images. The model was implemented in MATLAB R2023a.Performance was evaluated using accuracy, sensitivity, specificity, false positive rate, precision, and recognition time, with an 80-20% training-testing split. CNN achieved 95.86% accuracy, 96.44% sensitivity, 95.00% specificity, 5.00% false positive rate, 96.66% precision, and 99.90 s recognition time. WOA-CNN achieved 97.23% accuracy, 97.58% sensitivity, 96.70% specificity, 3.30% false positive rate, 97.80% precision, and 87.40 s recognition time.This research showed WOA-CNN outperformed CNN in all metrics. It is recommended for use in biometric authentication, security checkpoints, and forensic investigations.

In this paper, a feature selection system is introduced applies the whale optimization algorithm (WOA). WOA is a recently introduced meta-heuristic optimization algorithm that mimics the natural behavior of the humpback whales. The proposed model applies the wrapper-based method to reach the optimal subset of features. This technique was applied to find the best feature subset that maximizes the accuracy of the classification while preserving the minimum number of features. The proposed model is compared with the particle swarm optimization (PSO) and genetic algorithm (GA) using a number of assessment indicators on 16 different data-sets from UCI data repository. The results demonstrate the advantage of the introduced algorithm compared to the other optimizers.

Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.

This paper explains a systematic and dependable whale optimization algorithm (WOA) method for static economic load dispatch (SELD) and dynamic economic load (DELD) problems. This WOA technique was established because of the imitation of a streamlined collective structure. It was found to help solve optimization related problems in the matter of the result’s certainty and calculation time, although it can be performed on different problems. The developed technique is used for the SELD and DELD of three thermal generator unit problems. After comparing the behaviour of developed WOA technique with different optimization techniques like PSO, genetic algorithm and traditional optimization approaches, it was noticed that this technique is dependable and possibly succeed effectively over the other techniques currently used in various load dispatch problems. The results after comparison prove that the developed WOA technique can get quality results effectively for SELD and DELD problems in less computational time.

The optimized network reconfiguration and Distributed Generations (DG) sizing with allocation instantaneously via Particle Swarm Optimization (PSO) proposed a new way of allocation DG based on low voltage profile. This method consists of three steps. It started with categorized the switching sequences for radial network configuration while observe the P losses and the profile of voltage without DG. The second step is reconfiguration feeder for reduce losses via DGs allocation based on substations geographical location. The final step is sizing and allocation DGs at each bus with low voltage profile produced from the first step, used to mend the voltage profile and minimize the P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">losses</sub> also compared the result with the geographical based allocation results. The objective of this study is to mend the voltage profile while decreasing the P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">losses</sub> by using optimization technique considering network reconfiguration, DGs Sizing and allocation concurrently. Four cases are compared which is case 1 is the initial case and taken as a reference. All three stages are tested on standards IEEE 33 bus system by using Particle Swarm Optimization (PSO) technique in MATLAB software. This method proved that improvement of Plosses and voltage profile has been made by change of the switching topology with DGs sizing and allocation technique respectively.