Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

An autonomous microgrid that runs on renewable energy sources is presented in this article.It has a supercon-ducting magnetic energy storage(SMES)device,wind energy-producing devices,and an energy storage battery.However,because such microgrids are nonlinear and the energy they create varies with time,controlling and managing the energy inside them is a difficult issue.Fractional-order proportional integral(FOPI)controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance.The suggested dedicated control for the SMES comprises two loops:the outer loop,which uses the FOPI to regulate the DC-link voltage,and the inner loop,responsible for regulating the SMES current,is constructed using the intelligent FOPI(iFOPI).The FOPI+iFOPI parameters are best developed using the dandelion optimizer(DO)approach to achieve the optimum performance.The suggested FOPI+iFOPI controller’s performance is contrasted with a conventional PI controller for variations in wind speed and microgrid load.The optimal FOPI+iFOPI controller manages the voltage and frequency of the load.The behavior of the microgrid as a reaction to step changes in load and wind speed was measured using the proposed controller.MATLAB simulations were used to evaluate the recommended system’s performance.The results of the simulations showed that throughout all interruptions,the recommended microgrid provided the load with AC power with a constant amplitude and frequency.In addition,the required load demand was accurately reduced.Furthermore,the microgrid functioned incredibly well despite SMES and varying wind speeds.Results obtained under identical conditions were compared with and without the best FOPI+iFOPI controller.When utilizing the optimal FOPI+iFOPI controller with SMES,it was found that the microgrid performed better than the microgrid without SMES.

Characteristics of Biopellets Manufactured from Various Lignocellulosic Feedstocks as Alternative Renewable Energy Sources

The increased valorization of renewable and cost-effective lignocellulosic feedstocks represents a viable,sustainable,and eco-friendly approach toward the production of biopellets as alternative energy sources.The aim of this research work was to investigate and evaluate the feasibility of using various lignocellulosic raw materials,i.e.,raru(Cotylelobium melanoxylon),mangrove(Rhizophora spp.),sengon(Paraserianthes falcataria),kemenyan toba(Styrax sumatrana),oil palm(Elaeis guineensis),manau rattan(Calamus manan),and belangke bamboo(Gigantochloa pruriens)for manufacturing biopellets with different particle sizes.The raw materials used were tested for their moisture content,specific gravity,ash,cellulose,and lignin content.In addition,thermal analyses,i.e.,calorific values,thermogravimetric analysis(TGA),and differential scanning calorimetry(DSC),were performed.The following properties of the biopellets produced were investigated:moisture content,volatile matter,ash content,fixed carbon,density,and thermal analyses.Based on an analysis of the raw materials,raru had the lowest moisture content(12%)and ash content(1.5%)and the highest specific gravity(1.2).Markedly,palm oil stem had the highestα-cellulose(55%)and lignin(37%)content.In accordance with the SNI 8675:2018 standard requirements,biopellets with optimal properties(moisture content of 1.4%,ash content of 0.79%,density of 1.09 g/m^(3),calorific value of 4672 cal/g,and TGA residue of 13.9%),were manufactured from raru wood.

Hydroelectric and Hydrogen Storage Systems for Electric Energy Produced from Renewable Energy Sources

Renewable energy sources are essential formitigating the greenhouse effect and supplying energy to resource-scarce regions.However,their intermittent nature necessitates efficient storage solutions to enhance system efficiency and manage energy costs.This paper investigates renewable and clean storage systems,specifically examining the storage of electricity generated from renewable sources using hydropower plants and hydrogen,both of which are highly efficient and promising for future energy production and storage.The study utilizes extensive literature data to analyze the impact of various parameters on the cost per kWh of electricity production in hybrid renewable systems incorporating hydropower and hydrogen storage plants.Results indicate that these hybrid systems can store electricity efficiently and cost-effectively,with production costs ranging from 0.126 to 0.3$/kWh for renewablehydropower systems and 0.118 to 0.42$/kWh for renewable-hydrogen systems,with expected cost reductions over the next decade due to technological advancements and increased market adoption.The novelty of this study lies in its comprehensive comparison of hybrid renewable systems integrating hydropower and hydrogen storage,providing detailed cost analysis and future projections.It identifies key parameters influencing the cost and efficiency of these systems,offering insights into optimizing storage solutions for renewable energy.Moreover,this research underscores the potential of hybrid systems to reduce dependency on fossil fuels,particularly during peak demand periods,and emphasizes the importance of seasonal and geographic considerations in selecting energy sources.The study highlights the importance of policy support and investment in hybrid renewable systems and calls for further research into optimizing these systems for different seasonal and geographic conditions.Overall,the integration of renewable energy sources with hydropower and hydrogen storage offers a promising pathway to a sustainable,economical,and resilient energy future.

A Two-Layer Active Power Optimization and Coordinated Control for Regional Power Grid Partitioning to Promote Distributed Renewable Energy Consumption

With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.

ANovel Non-Isolated Cubic DC-DC Converter with High Voltage Gain for Renewable Energy Power Generation System

In recent years,switched inductor(SL)technology,switched capacitor(SC)technology,and switched inductor-capacitor(SL-SC)technology have been widely applied to optimize and improve DC-DC boost converters,which can effectively enhance voltage gain and reduce device stress.To address the issue of low output voltage in current renewable energy power generation systems,this study proposes a novel non-isolated cubic high-gain DC-DC converter based on the traditional quadratic DC-DC boost converter by incorporating a SC and a SL-SC unit.Firstly,the proposed converter’s details are elaborated,including its topology structure,operating mode,voltage gain,device stress,and power loss.Subsequently,a comparative analysis is conducted on the voltage gain and device stress between the proposed converter and other high-gain converters.Then,a closed-loop simulation system is constructed to obtain simulation waveforms of various devices and explore the dynamic performance.Finally,an experimental prototype is built,experimental waveforms are obtained,and the experimental dynamic performance and conversion efficiency are analyzed.The theoretical analysis’s correctness is verified through simulation and experimental results.The proposed converter has advantages such as high voltage gain,low device stress,high conversion efficiency,simple control,and wide input voltage range,achieving a good balance between voltage gain,device stress,and power loss.The proposed converter is well-suited for renewable energy systems and holds theoretical significance and practical value in renewable energy applications.It provides an effective solution to the issue of low output voltage in renewable energy power generation systems.

Estimation of Landfill Gas and Its Renewable Energy Potential from the Polesgo Controlled Landfill Using First-Order Decay (FOD) Models

Methane generation in landfills and its inadequate management represent the major avoidable source of anthropogenic methane today. This paper models methane production and the potential resources expected (electrical energy production and potential carbon credits from avoided CH4 emissions) from its proper management in a municipal solid waste landfill located in Ouagadougou, Burkina Faso. The modeling was carried out using two first-order decay (FOD) models (LandGEM V3.02 and SWANA) using parameters evaluated on the basis of the characteristics of the waste admitted to the landfill and weather data for the site. At the same time, production data have been collected since 2016 in order to compare them with the model results. The results obtained from these models were compared to experimental one. For the simulation of methane production, the SWANA model showed better consistency with experimental data, with a coefficient of determination (R²) of 0.59 compared with the LandGEM model, which obtained a coefficient of 0.006. Thus, despite the low correlation values linked to the poor consistency of experimental data, the SWANA model models methane production much better than the LandGEM model. Thus, despite the low correlation values linked to the poor consistency of the experimental data, the SWANA model models methane production much better than the LandGEM V3.02 model. It was noted that the poor consistency of the experimental data justifies these low coefficients, and that they can be improved in the future thanks to ongoing in situ measurements. According to the SWANA model prediction, in 27 years of operation a biogas plant with 33% electrical efficiency using biogas from the Polesgo landfill would avoid 1,340 GgCO2e. Also, the evaluation of revenues due to electricity and carbon credit gave a total revenue derived from methane production of US$27.38 million at a cost of US$10.5/tonne CO2e.

Renewable Energy: Prospects and Challenges in Bangladesh

Among expert scientists and politicians, there is increasing agreement that it is absolutely necessary to reduce the emission of greenhouse gas (GHG) to lessen the severity of climate change. Although little, renewable energy sources currently reduce GHG that are being emitted from the energy industries. According to the majority of long-term energy estimates, renewable energy will be a substantial addition to the supply of energy worldwide by the end of this century, as capacity of renewable energy is gradually increasing in the early decades. However, developing nations like Bangladesh are largely reliant on pricey imported energy supplies (coal, gas, and oil) that lay a heavy weight on the country’s economy. Also, air pollution growing in importance as a national and international environmental issue. Regarding the development of clean and sustainable energy, renewable energy sources seem to be among the most practical and efficient alternatives, in both Bangladesh and globally. The geographic advantages of Bangladesh allow for widespread usage of the majority of such renewable energy sources. The comparative potential and use of fossil fuels against renewable energy sources globally and in Bangladesh is explored in this review.

Application of Power Electronics Converters in Renewable Energy

Against the backdrop of global energy shortages and increasingly severe environmental pollution,renewable energy is gradually becoming a significant direction for future energy development.Power electronics converters,as the core technology for energy conversion and control,play a crucial role in enhancing the efficiency and stability of renewable energy systems.This paper explores the basic principles and functions of power electronics converters and their specific applications in photovoltaic power generation,wind power generation,and energy storage systems.Additionally,it analyzes the current innovations in high-efficiency energy conversion,multilevel conversion technology,and the application of new materials and devices.By studying these technologies,the aim is to promote the widespread application of power electronics converters in renewable energy systems and provide theoretical and technical support for achieving sustainable energy development.

Renewable Energy Development in OAPEC Countries: History, Current Status, and Outlook

The renewable energy industry has grown its contribution to the global energy mix, particularly in terms of electricity generation. This study investigates the implications of an increasing renewable energy share on OAPEC countries and proposes a comprehensible policy strategy for the region. Four main topics are discussed: scientific and engineering principles of renewable energy utilization, current strategies for electricity generation in each OAPEC member country, economic and environmental implications of the energy transition under two future scenarios, as well as political interactions between oil-consuming and oil-producing countries. Based on this study, realistic and cost-effective strategies are proposed for OAPEC countries to better leverage their significant renewable energy resources while stabilizing fossil fuel supplies and strengthening their position in the global energy market. To mitigate the negative impacts of the energy transition, OAPEC countries are encouraged to take the following steps: 1) Developing renewable energy in conjunction with fossil fuel resources to reduce local demand for fossil fuel and increase the supply for exportation;2) Reviewing economic policies, environmental regulations, and carbon taxes imposed by oil-consuming countries;3) Increasing investment in renewable energy infrastructure;4) Cooperating to achieve a balance between economic development and environmental protection.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

Low-Carbon Dispatch of an Integrated Energy System Considering Confidence Intervals for Renewable Energy Generation

Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation,this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side.A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation,with the overarching goal of optimizing the system for low-carbon operation.To begin with,an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation mechanisms exhibited by the source-side carbon capture power plant within the context of integrated and adaptable operational paradigms.Drawing from this analysis,a model is devised to represent the adjustable resources on the charge-storage side,predicated on the principles of electro-thermal coupling within the energy system.Subsequently,the dissimilarities in the confidence intervals of renewable energy generation are considered,leading to the proposition of a flexible upper threshold for the confidence interval.Building on this,a low-carbon dispatch model is established for the integrated energy system,factoring in the margin allowed by the adjustable resources.In the final phase,a simulation is performed on a regional electric heating integrated energy system.This simulation seeks to assess the impact of source-load-storage coordination on the system’s low-carbon operation across various scenarios of reduction margin reserves.The findings underscore that the proactive scheduling model incorporating confidence interval considerations for reduction margin reserves effectively mitigates the uncertainties tied to renewable energy generation.Through harmonized orchestration of source,load,and storage elements,it expands the utilization scope for renewable energy,safeguards the economic efficiency of system operations under low-carbon emission conditions,and empirically validates the soundness and efficacy of the proposed approach.

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research,a modified fractional order proportional integral derivate(FOPID)control method is proposed for the photovoltaic(PV)and thermoelectric generator(TEG)combined hybrid renewable energy system.The faster tracking and steady-state output are aimed at the suggested maximum power point tracking(MPPT)control technique.The derivative order number(μ)value in the improved FOPID(also known as PIλDμ)control structure will be dynamically updated utilizing the value of change in PV array voltage output.During the transient,the value ofμis changeable;it’s one at the start and after reaching the maximum power point(MPP),allowing for strong tracking characteristics.TEG will use the freely available waste thermal energy created surrounding the PVarray for additional power generation,increasing the system’s energy conversion efficiency.A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit.The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation(P&O)type MPPT control method.The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source.There is also a better control action and a faster response.

Evolving National Security: A Roadmap from Present to Future in Renewable and Nonrenewable Energy Policies (A Technical Review)

This comprehensive exploration delves into the intricate dynamics of national security policies in the realm of renewable and nonrenewable energy sources.From the present landscape characterized by the diversification of energy portfolios to the long-term vision encompassing nuclear fusion,this article navigates through the nuanced interplay of technology,resilience,and environmental responsibility.The synthesis of established nuclear fission technologies and evolving renewable sources forms the cornerstone of a strategic approach,addressing challenges and opportunities to ensure a secure,sustainable energy future.

A Comparative Analysis of the Sustainable Growth of Global Hydro, Solar, and Wind Power Systems (Renewable Energy Systems)

The unfettered reliance on fossil fuels for centuries has pushed the world to the brink of severe environmental crises. While individual studies on renewable energy generation capacity have been conducted, a comprehensive analysis is lacking. This study aims to address this gap by providing a comparative analysis of three major renewable energy sources—hydro, solar, and wind— and their current global utilization statistics. Additionally, it will examine the efficacy of fossil fuels and their detrimental impact on the environment. Global warming and its associated health consequences on the ecosystem are rapidly escalating. Without a complete decarbonization of our energy systems, environmental deterioration is poised to continue at an alarming rate. Fortunately, a plethora of traditional and renewable energy resources exist that have minimal or no environmental impact and have been available for years. However, these resources remain largely untapped. The full potential of RE resources hinges on the development of sustainable technologies to harness their energy to their fullest capacity. This study delves into the current global and regional RE utilization from 2013 to 2022, based on data from the International Renewable Energy Agency (IRENA) 2023. The focus is limited to the three primary renewable energy sources with the highest harnessing capacity in recent times. Employing appropriate mathematical analyses, the results reveal exponential growth in renewable energy, with an average annual generating capacity of 2353550.7 MW over the past decade. Hydroelectric power, solar power, and wind, among others, have played a significant role in the global penetration of renewable energy systems. The changing dynamics have propelled these RE resources into the spotlight in recent years, owing to their sustainability and environmental friendliness.

Air Pollution, Global Warming and Difficulties to Replace Fossil Fuel with Renewable Energy

Since the Industrial Revolution, greenhouse gas (GHG) emissions have greatly increased with the increased use of fossil fuels, leading to air pollution and global warming. We present the researches on air pollution and the use of fossil fuels in north China, the economic zone of Changsha-Zhuzhou-Xiangtan and the economic zone of the Pearl River Delta region. Researches indicate that the use of fossil fuels has been the main source of air pollution in the three regions. We present researches on global mean surface temperature (GMST) with the rise of carbon dioxide concentration (CDC) and global fossil fuel consumption (GFFC);researches indicate that the rise in CDC can account for 91% of the rise in GMST, and GFFC can account for 90% of the rise in GMST. We analyse the factors that bring about air pollution and temperature rise, they are the use of fossil fuels and deforestation. It is critically important to replace fossil fuels with clean energy, but renewable energy has also disadvantages. The world faces difficulties in solving air pollution and global warming, so governments of the world should cooperate to solve the technologies of clean energy, and preserve the forests and the natural environment.

Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and reliable.The RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy demands.Since energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power resources.Predicting energy consumption of RE and NRE sources using effective models becomes necessary.With this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power sources.Actual data may influence the prediction performance of the results in prediction approaches.The proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter optimization.In addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL models.The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

Renewable energy is created by renewable natural resources such as geothermal heat,sunlight,tides,rain,and wind.Energy resources are vital for all countries in terms of their economies and politics.As a result,selecting the optimal option for any country is critical in terms of energy investments.Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming.In the present work,the authors suggest fuzzy multi-characteristic decision-making approaches for renew-able energy source selection,and fuzzy set theory is a valuable methodology for dealing with uncertainty in the presence of incomplete or ambiguous data.This study employed a hybrid method for order of preference by resemblance to an ideal solution based on fuzzy analytical network process-technique,which agrees with professional assessment scores to be linguistic phrases,fuzzy numbers,or crisp numbers.The hybrid methodology is based on fuzzy set ideologies,which calculate alternatives in accordance with professional functional requirements using objective or subjective characteristics.The best-suited renewable energy alternative is discovered using the approach presented.

A Joint Optimization Algorithm for Renewable Energy System

Energy sustainability is a hot topic in both scientific and political cir-cles.To date,two alternative approaches to this issue are being taken.Some peo-ple believe that increasing power consumption is necessary for countries’economic and social progress,while others are more concerned with maintaining carbon consumption under set limitations.To establish a secure,sustainable,and economical energy system while mitigating the consequences of climate change,most governments are currently pushing renewable growth policies.Energy mar-kets are meant to provide consumers with dependable electricity at the lowest pos-sible cost.A profit-maximization optimal decision model is created in the electric power market with the combined wind,solar units,loads,and energy storage sys-tems,based on the bidding mechanism in the electricity market and operational principles.This model utterly considers the technological limits of new energy units and storages,as well as the involvement of new energy and electric vehicles in market bidding through power generation strategy and the output arrangement of the virtual power plant’s coordinated operation.The accuracy and validity of the optimal decision-making model of combined wind,solar units,loads,and energy storage systems are validated using numerical examples.Under multi-operating scenarios,the effects of renewable energy output changes on joint sys-tem bidding techniques are compared.

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this problemby using its regulation and flexibility, and is considered to be an ideal platform.The traditional method of computing total transfer capability is difficult due tothe central integration of wind farms. As a result, the differential evolutionextreme learning machine is offered as a data mining approach for extractingoperating rules for the total transfer capability of tie-lines in wind-integratedpower systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, aknowledge base for total transfer capability operating rule mining is created.Then, a novel method is used to filter redundant characteristics and find featuresthat are closely associated to the total transfer capability in order to decrease theultra-high dimensionality of operational features. Finally, by feeding the trainingdata into the proposed algorithm, the total transfer capability operation rules arederived from the knowledge base. It can be seen that, the proposed algorithmcan optimize the system performance with good accuracy and generality, according to numerical data.