A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics

There is a growing interest in the study development of artificial intelligence and machine learning,especially regarding the support vector machine pattern classification method.This study proposes an enhanced implementation of the well-known whale optimisation algorithm,which combines chaotic and opposition-based learning strategies,which is adopted for hyper-parameter optimisation and feature selection machine learning challenges.The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation.The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer,diabetes,and erythemato-squamous dataset.The performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics,including another improved whale optimisation approach,particle swarm optimisation algorithm,bacterial foraging optimisation algorithms,and genetic algorithms.Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.

Optimal proportioning of iron ore in sintering process based on improved multi-objective beluga whale optimisation algorithm

Proportioning is an important part of sintering,as it affects the cost of sintering and the quality of sintered ore.To address the problems posed by the complex raw material information and numerous constraints in the sintering process,a multi-objective optimisation model for sintering proportioning was established,which takes the proportioning cost and TFe as the optimisation objectives.Additionally,an improved multi-objective beluga whale optimisation(IMOBWO)algorithm was proposed to solve the nonlinear,multi-constrained multi-objective optimisation problems.The algorithm uses the con-strained non-dominance criterion to deal with the constraint problem in the model.Moreover,the algorithm employs an opposite learning strategy and a population guidance mechanism based on angular competition and two-population competition strategy to enhance convergence and population diversity.The actual proportioning of a steel plant indicates that the IMOBWO algorithm applied to the ore proportioning process has good convergence and obtains the uniformly distributed Pareto front.Meanwhile,compared with the actual proportioning scheme,the proportioning cost is reduced by 4.3361¥/t,and the TFe content in the mixture is increased by 0.0367%in the optimal compromise solution.Therefore,the proposed method effectively balances the cost and total iron,facilitating the comprehensive utilisation of sintered iron ore resources while ensuring quality assurance.

Cloud-based data security transactions employing blowfish and spotted hyena optimisation algorithm

Because of its on-demand servicing and scalability features in cloud computing,security and confidentiality have converted to key concerns.Maintaining transaction information on thirdparty servers carries significant dangers so that malicious individuals trying for illegal access to information data security architecture.This research proposes a security-aware information transfer in the cloud-based on the blowfish algorithm(BFA)to address the issue.The user is verified initially with the identification and separate the imported data using pattern matching technique.Further,BFA is utilised to encrypt and save the data in cloud.This can safeguard the data and streamline the proof so that client cannot retrieve the information without identification which makes the environment secure.The suggested approach’s performance is evaluated using several metrics,including encryption time,decryption time,memory utilisation,and runtime.Compared to the existing methodology,the investigational findings clearly show that the method takes the least time to data encryption.

3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However,not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore,there is dire need to integrate metaheuristic for general acceptability.To address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.

Dispersed Wind Power Planning Method Considering Network Loss Correction with Cold Weather

In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.

EEG signal artefact removal using flower pollination fractional calculus optimisation

Purpose-The main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition.Initially,pre-processing is done on EEG signal for quality improvement.Then,by using wavelet transform(WT)feature extraction is done.The artefacts present in the EEG are removed using deep convLSTM.This deep convLSTM is trained by proposed fractional calculus based flower pollination optimisation algorithm.Design/methodology/approach-Nowadays’EEG signals play vital role in the field of neurophysiologic research.Brain activities of human can be analysed by using EEG signals.These signals are frequently affected by noise during acquisition and other external disturbances,which lead to degrade the signal quality.Denoising of EEG signals is necessary for the effective usage of signals in any application.This paper proposes a new technique named as flower pollination fractional calculus optimisation(FPFCO)algorithm for the removal of artefacts fromEEGsignal through deep learning scheme.FPFCOalgorithmis the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep convLSTM.The existed FPO algorithm is used for solution update through global and local pollinations.In this case,the fractional calculus(FC)method attempts to include the past solution by including the second order derivative.As a result,the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization(FPO)method.Initially,5 EEGsignals are contaminated by artefacts such asEMG,EOG,EEGand randomnoise.These contaminatedEEG signals are pre-processed to remove baseline and power line noises.Further,feature extraction is done by using WTand extracted features are applied to deep convLSTM,which is trained by proposed fractional calculus based flower pollination optimisation algorithm.FPFCO is used for the effective removal of artefacts from EEG signal.The proposed technique is compared with existing techniques in terms of SNR and MSE.Findings-The proposed technique is compared with existing techniques in terms of SNR,RMSE and MSE.Originality/value-100%.

NDRank: optimised parallel search for weather analogues

Global meteorology data are now widely used in various areas, but one of its applications, weather analogues, still require exhaustive searches on the whole historical data. We present two optimisations for the state-of-the-art weather analogue search algorithms: a parallelization and a heuristic search. The heuristic search (NDRank) limits of the final number of results and does initial searches on a lower resolution dataset to find candidates that, in the second phase, are locally validated. These optimisations were deployed in the Cloud and evaluated with ERA5 data from ECMWF. The proposed parallelization attained speedups close to optimal, and NDRank attains speedups higher than 4. NDRank can be applied to any parallel search, adding similar speedups. A substantial number of executions returned a set of analogues similar to the existing exhaustive search and most of the remaining results presented a numerical value difference lower than 0.1%. The results demonstrate that it is now possible to search for weather analogues in a faster way (even compared with parallel searches) with results with little to no error. Furthermore, NDRank can be applied to existing exhaustive searches, providing faster results with small reduction of the precision of the results.