Bacterial Foraging Optimization Algorithm Research Articles

Optimal Placement of TCSC and DPFC on Nigeria Power Transmission Network for Capacity Enhancement

The increasing demand for electrical energy and the high costs and challenges associated with building new transmission lines highlight the need to optimize existing power transmission networks. This study focuses on optimizing or enhancing Nigeria's 28-bus power transmission system using Flexible Alternating Current Transmission System (FACTS) devices, specifically, Dynamic Power Flow Controller (DPFC) and Thyristor-Controlled Series Compensator (TCSC). This research employed the Bacterial Foraging Optimization Algorithm (BFOA) in MATLAB to determine the optimal placement and sizing of DPFC and TCSC to minimize power losses, voltage deviations, and installation costs. Simulation results were compared, before and after incorporating the FACTS devices. In the base case, 32% of the buses had voltage profiles outside the acceptable range (0.95 to 1.05 p.u.), with real power loss of 602.91 kW and reactive power loss of 4559.69 kVAR. After optimally placing two DPFC and two TCSC devices in the system, all bus voltages were regulated to fall between 0.95 and 1.06 p.u., while real and reactive power losses were reduced by approximately 84.6% and 84.7%, respectively. This demonstrates a significant improvement in the efficiency and capacity of the power transmission network.

Hierarchical Optimization Configuration Strategy of Synchronous Condenser in High Penetration Wind Power Sending Systems

The increasing deployment of large-scale wind turbines in place of conventional generators is expected to lead to the dominance of asynchronous power sources in future power systems, further accelerating the trend toward grid electrification. As a result, the ability of power sources to support system voltage and frequency is gradually diminishing. Synchronous condensers (SCs), which are synchronous machines operating without prime movers, serve as effective devices for providing both dynamic voltage support and inertia. They can significantly enhance the system’s capacity to maintain voltage and frequency stability. However, most existing studies on the optimization of synchronous condenser configurations tend to focus on only one aspect at a time rather than addressing both simultaneously, limiting the full potential of these devices. Optimizing either the voltage or frequency in isolation often results in suboptimal improvements in the other. Moreover, the simultaneous optimization of both voltage and frequency can lead to non-convergent outcomes, complicating the search for an optimal solution. To address this, the paper proposes a hierarchical optimization strategy for synchronous condenser configuration aimed at enhancing both voltage and frequency stability. First, the connection sites for the synchronous condensers are determined based on short-circuit ratio (SCR) constraints. Next, an outer layer optimization model is developed to minimize the total installed capacity of the condensers while taking into account the SCR and transient overvoltage levels as constraints. Following this, an inner layer optimization model is introduced, incorporating a rate of change in the frequency and maximum frequency deviation as constraints. The model is solved using the bacterial foraging optimization algorithm (BFOA). Finally, a case study of a power grid with a high proportion of wind power validates the effectiveness of the proposed synchronous condenser configuration strategy. Compared to traditional methods, the total required capacity of synchronous condensers was significantly reduced.

Improved Bacterial Foraging Optimization Algorithm with Machine Learning-Driven Short-Term Electricity Load Forecasting: A Case Study in Peninsular Malaysia

Accurate electricity demand forecasting is crucial for ensuring the sustainability and reliability of power systems. Least square support vector machines (LSSVM) are well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. The IBFOA based on the sine cosine equation addresses the limitations of fixed chemotaxis constants in the original bacterial foraging optimization algorithm (BFOA), enhancing its exploration and exploitation capabilities. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using mean absolute percentage error (MAPE) as the objective function. The comparative analysis demonstrates the model, achieving the highest determination coefficient (R2) of 0.9880 and significantly reducing the average MAPE value by 28.36%, 27.72%, and 5.47% compared to the deep neural network (DNN), LSSVM, and LSSVM-BFOA, respectively. Additionally, IBFOA exhibits faster convergence times compared to BFOA, highlighting the practicality of LSSVM-IBFOA for short-term load forecasting.

Displacement prediction of tunnel entrance slope based on LSSVM and bacterial foraging optimization algorithm

In order to realize the effective prediction of landslide risk in the tunnel entrance area, an multivariate time series model is established on the basis of the traditional model, taking temperature and rainfall factors as additional input indicators. Bacterial foraging optimization algorithm (BFOA) is used to search the global optimal solution of the key parameters \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma$$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma^{2}$$\\end{document} of least squares support vector machine (LSSVM) to improve its regression accuracy, and the evolved LSSVM is used to describe the aforementioned multivariate time series model. At the same time, a remote real-time internet of things (IoT) monitoring system for the tunnel entrance section, including monitoring indicators such as surface subsidence, temperature, and rainfall, has also been designed and implemented, providing a stable and accurate data source for the realization of this prediction model. Based on the engineering measurement data, the accuracy of the established model is checked and analyzed, the optimal value of historical data amount is determined to be 5 days, and the optimal value of prediction step is 1 day. The research results are applied in the construction of Wendong tunnel of Molin expressway, Yunnan, China. Practice shows that the prediction results of the multivariate time series model established in this study is accurate. This method can realize the prediction and early warning of slope risk, which provides a effective technical means for risk control of tunnel portal section.

An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease

Meibomian Gland Dysfunction (MGD) and Dry Eye Disease (DED) comprise two of the most significant eye diseases, impacting millions of sufferers worldwide. Several etiological factors influence the early symptoms of DED. Early diagnosis and treatment of erectile dysfunction may significantly improve the Quality of Life (QoL) for people. The current study introduces the ESAE-ODNN, an improved stacked autoencoder-aided optimised deep neural network, as a new way to predict DED using feature selection (FS), feature extraction (FE), and classification. The approach described here is novel because it merges chaotic maps into FS, employs SLSTM-STSA for improved classification accuracy (CA), and optimizes with the adaptive quantum rotation of the Enhanced Quantum Bacterial Foraging Optimisation Algorithm (EQBFOA). The present study enhances prediction functions by extracting MGD-related features and complicated relationships from the DED dataset. To ensure essential feature identification, the ESAE minimizes irrelevant and redundant features. To predict the DED, the ESAE first applies FE and then implements an ODNN classifier. This method fine-tunes the ODNN framework to enhance the effectiveness of the classification. The proposed ESAE-ODNN classification system efficiently assists in the early diagnosis of DED. Combining advanced Deep Learning (DL) methods with optimization can help us understand MGD features better and sort the data with the best accuracy (96.34%). The experimental evaluation with relevant performance metrics indicates that the proposed method is efficient in diverse aspects: accurate identification, reduced complexity, and fine-tuned performance. The ESAE-ODNN’s robustness in handling intricate feature indications and high-dimensional data outperforms the existing state-of-the-art techniques.

Enhanced Management of Unified Energy Systems Using Hydrogen Fuel Cell Combined Heat and Power with a Carbon Trading Scheme Incentivizing Emissions Reduction

In the quest to achieve “double carbon” goals, the urgency to develop an efficient Integrated Energy System (IES) is paramount. This study introduces a novel approach to IES by refining the conventional Power-to-Gas (P2G) system. The inability of current P2G systems to operate independently has led to the incorporation of hydrogen fuel cells and the detailed investigation of P2G’s dual-phase operation, enhancing the integration of renewable energy sources. Additionally, this paper introduces a carbon trading mechanism with a refined penalty–reward scale and a detailed pricing tier for carbon emissions, compelling energy suppliers to reduce their carbon footprint, thereby accelerating the reduction in system-wide emissions. Furthermore, this research proposes a flexible adjustment mechanism for the heat-to-power ratio in cogeneration, significantly enhancing energy utilization efficiency and further promoting conservation and emission reductions. The proposed optimization model in this study focuses on minimizing the total costs, including those associated with carbon trading and renewable energy integration, within the combined P2G-Hydrogen Fuel Cell (HFC) cogeneration system. Employing a bacterial foraging optimization algorithm tailored to this model’s characteristics, the study establishes six operational modes for comparative analysis and validation. The results demonstrate a 19.1% reduction in total operating costs and a 22.2% decrease in carbon emissions, confirming the system’s efficacy, low carbon footprint, and economic viability.

Optimized Deep Neural Network Based Load Balancing in Fog Computing with Robust Dynamic Scheduling Algorithm

The load balancing (LB) in FOG architecture is a formidable task with the availability of minimal resources. This field encounters enormous studies that portray the routing of task between the fog physical devices and cloud nodes. The presence of different types of heterogeneity devices are at helm of difficult scheduling process. To allocate LB based on the available resource we propose a novel Deep Neural Network (DNN) based Tuna swarm strategy based Bacterial Foraging optimization algorithm (TBFO) which employs three stages such as monitoring the fog resources, Classification based on deep learning technique and dynamic scheduler that are optimized. With the dynamic scheduling algorithm this work aims to provide LB in real time application. The first stage is to monitor the resources of server and amassed in fog resource table. The next step is to identify the unerring server and is effectuated with the proposed DNN technique. The last stage is to allocate the process to the selected server with the TBFO. It certifies the robust continuous services in the fog LB. Simulations are conducted and compared the outcomes with the existing works and ensures effective load balancing, make span and resource utilization.

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

Optimizing the Efficiency of Photovoltaic Distributed Generation in the Distribution System

This article studies the influence of distributed generation (DG), specifically the influence of photovoltaic (PV) in the distribution system. The particle swarm optimization algorithm (PSO) will be applied to determine the best capacity and location of PV on a test system of EEE 33 nodes so that active power loss is minimized, and the voltage profile is improved. The performance of the applied method is evaluated by comparing its results to those from some previous methods, including the Genetic Algorithm (GA), the Bacterial Foraging Optimization Algorithm (BFOA), and the Backtracking Search Optimization Algorithm (BSOA). As a result, it proved that the proposed method is better than others in terms of processing time, voltage profile, and minimization system capacity loss. In addition, the main contribution of the study is to give detailed solutions for operators in installing how many PVs in the power system can satisfy economic and technical aspects.

Multi-Objective Dynamic Reconstruction of Distributed Energy Distribution Networks Based on Stochastic Probability Models and Optimized Beetle Antennae Search

In the dynamic optimization problem of the distribution network, a dynamic reconstruction method based on a stochastic probability model and optimized beetle antennae search is proposed. By implementing dynamic reconstruction of distributed energy distribution networks, the dynamic regulation and optimization capabilities of the distribution network can be improved. In this study, a random probability model is used to describe the uncertainty in the power grid. The beetle antennae search is used for dynamic multi-objective optimization. The performance of the beetle antennae search is improved by combining it with the simulated annealing algorithm. According to the results, the optimization success rate of the model was 98.7%. Compared with the discrete binary particle swarm optimization algorithm and bacterial foraging optimization algorithm, it was 9.3% and 26.1% faster, respectively. For practical applications, this model could effectively reduce power grid transmission losses, with a reduction range of 16.7–18.6%. Meanwhile, the charging and discharging loads were effectively reduced, with a reduction range of 16.2–19.7%. Therefore, this method has significant optimization effects on actual power grid operation. This research achievement contributes to the further development of dynamic reconstruction technology for distribution networks, improving the operational efficiency and stability of the power grid. This has important practical significance for achieving green and intelligent operation of the power system.

Improved adaptive neuro-fuzzy inference system with bacterial foraging optimization algorithm for suspended sediment concentration estimation

The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFIS-FCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance.

Phase Retrieval for Radar Constant–Modulus Signal Design Based on the Bacterial Foraging Optimization Algorithm

Optimizing the energy spectrum density (ESD) of a transmitted waveform can improve radar performance. The design of a time–domain constant–modulus signal corresponding to the transmitted waveform ESD is practically important because constant–modulus signals can maximize transmission power and meet the hardware requirements of radar transmitters. Here, we present a time–domain signal design under dual constraints of energy and constant modulus. The mutual information (MI)–based waveform design method is used to design transmitted waveform ESD under the energy constraint. Then, the bacterial foraging optimization algorithm (BFOA) is proposed to design the time–domain constant–modulus signal. We use minimum mean square error (MMSE) in the frequency domain as the cost function. The BFOA monotonously decreases the MMSE with increasing iterations, which makes the ESD of the time–domain constant–modulus signal close to the MI–based optimal waveform ESD. The simulation results indicate that the proposed algorithm has advantages, including insensitivity to initial phases, rapid convergence, smaller MI loss, and MMSE compared with the iterative reconstruction algorithm.

Simulation Research on Logistics Distribution Path Based on Colony Intelligence Optimisation

Abstract In light of the growing emphasis on “logistics integration,” this paper contends that the logistics network system requires comprehensive integration and optimization. We propose the use of the Bacterial Foraging Optimization (BFO) algorithm to enhance the Vehicle Routing Problem (VRP). The penalty function method is employed to direct the BFO algorithm in identifying the optimal vehicle path encoding and to improve the convergence operations for logistics distribution. This study analyzes the fundamental characteristic elements of the VRP, introduces a basic model for the VRP, and develops a logistics distribution path optimization model that minimizes the total cost by thoroughly considering the expenses associated with cold chain low-carbon logistics distribution. A simulation environment is established, and algorithm parameters are set to determine the optimal solutions for both the BFO and the Improved BFO (IBFO) algorithms in the test function. An example is selected for practical application, wherein the design of the distribution center and points is carried out, and the colony intelligence optimization algorithm is applied to simulate the logistics and distribution paths. Combined with the specific case of cold chain distribution of food, the optimization of the cold chain logistics distribution path is solved. In the case study, when the carbon penalty coefficient is ε = 15, the optimal logistics and distribution site selection program is Guangxi and Guizhou, and the total cost of the supply chain is 16,363,365.92 yuan. At this time, the supply chain cost is optimal. Compared with the carbon penalty coefficient ε = 20, the economic cost increased by 70152.24 yuan, but the carbon emission decreased by 1890.9 kg. The optimization of logistics and distribution processes encourages enterprises to develop strategies that promote low carbon and environmental protection and achieve better economic and ecological outcomes.

An efficient reconfigurable FIR filter design with coefficient optimization using a modified bacterial foraging optimization algorithm

ABSTRACT The digital filters play a significant role in the field of digital signal processing (DSP). The finite impulse response (FIR) filter is an attractive choice because of the ease of design and good stability. The digital filters have a wide variety of applications such as signal processing, control systems, telecommunication, etc. They are better than the analogue filters due to their performance. In recent times, software radios have achieved attention owing to requirements for integrated and reconfigurable communication systems. Hence, reconfigurations have emerged as a significant problem in the designs of FIRs. To match the frequencies of DSP applications, higher-order FIRs are required. If length of filters rises, addition and multiplication operations also increase. This paper proposes an efficient hardware design of RFIR that employs modified bacterial foraging optimizations (MBFOs) and common sub-expression eliminations (CSEs) in its executions. MBFOs output restricted counts of filter coefficients with sums of signed-power-of-two (SPT) terms while maintaining the quality of filtered responses. On obtaining coefficients, eliminations are executed by CSEs where hardware complexities are determined in terms of adders. Model sim software validated RFIRs using the Verilog code. The proposed design of RFIRs was compared with existing designs in terms of power usages, frequencies and areas.

Maximum Torque per Ampere Technique for Switched Reluctance Motor Drive

Numerous researchers have investigated switched reluctance motors (SRMs) as a competitor to other electrical motors for usage in electric and hybrid vehicle appliances. SRMs are fault-tolerant and have a broad operating speed range. However, they have numerous drawbacks, such as high nonlinearity in machine models, complex control, and torque ripple. This study aims to improve the SRM drive system to attain maximum torque per ampere in a wide speed range. In the design and control of SRMs, maximizing torque per ampere (MTPA) is a crucial objective. MTPA refers to the strategy of achieving the maximum possible torque output for a given current input (amperes). This is essential for enhancing the efficiency and performance of the motor. The paper focuses on achieving MTPA in an SRM drive system-based commutation angle control. Utilizing a bacterial foraging optimization algorithm (BFOA), the suitable commutation angles at each operation point are determined. The intended technique is straightforward to realize and does not require complex algorithms. Simulation comparisons on an 8/6 SRM are also presented to validate the intended control technique. The results indicate that the MTPA has been enhanced by 6.961% compared to the conventional method.

Robust integration of fault estimation and sliding mode fault‐tolerant control for interconnected systems against sensor fault

AbstractThis paper provided a robust strategy for controlling nonlinear interconnected large‐scale systems subject to a sensor fault. The inherent challenge in controlling such systems is to sustain robust closed‐loop stability and performance in nonlinear interactions, faults, and exogenous inputs. A new closed‐loop structure has been devised to tackle this challenge by integrating the sliding mode controller with the unknown input observer. By exploiting the decoupling capability of the controller and observer, this integration ensures the robustness of the closed‐loop system against the simultaneous effect of interaction, fault, and disturbance. In this context, the unknown input observer has been constructed to supply the controller with an accurate sensor fault assessment despite additional unknown inputs. A novel approach is proposed for designing a decentralized fault‐tolerant control system that utilizes sliding mode control and proportional‐integral‐derivative controller tuning via a bacterial foraging optimization algorithm to compensate for the fault effect, leading to a robust output performance. The observer and controller gains are accomplished by utilizing the H∞ performance and linear matrix inequality formulation. The Lyapunov technique is used to demonstrate stability. A power system model emphasizes the proposed approach's robustness and effectiveness. The system performance with and without the proposed controller was compared, where the simulation results show a fast reaction to offset undesirable impacts, whereas the state estimation error approaches to zero in each subsystem.

An Improved Adaptive Spiral Dynamic Algorithm for Global Optimization

This paper proposes a new strategy to enhance the performance and accuracy of the Spiral dynamic algorithm (SDA) for use in solving real-world problems by hybridizing the SDA with the Bacterial Foraging optimization algorithm (BFA). The dynamic step size of SDA makes it a useful exploitation approach. However, it has limited exploration throughout the diversification phase, which results in getting trapped at local optima. The optimal initialization position for the SDA algorithm has been determined with the help of the chemotactic strategy of the BFA optimization algorithm, which has been utilized to improve the exploration approach of the SDA. The proposed Hybrid Adaptive Spiral Dynamic Bacterial Foraging (HASDBF) algorithm is designed so that the chemotaxis phase of bacteria represents the exploration part of the search operation. In contrast, the SDA represents the exploitation part. Additionally, to improve search operation efficiency, the spiral model's radius and angular displacement are adaptively set according to a linear correlation concerning the fitness value. An additional phase, the elimination and dispersal phase, is obtained from BFA and added to the end of the SDA. This phase aims to improve the algorithm's final solution's accuracy by enhancing the algorithm's search strategy and performance. Simulation tests are run on unimodal and multimodal standard benchmark functions to verify the proposed algorithm. The proposed algorithm significantly outperforms SDA and Adaptive SDA (ASDA) algorithms regarding fitness value and accuracy.

Kinetic Gas Molecule Optimization based Cluster Head Selection Algorithm for minimizing the Energy Consumption in WSN

As the amount of low-cost and low-power sensor nodes increases, so does the size of a wireless sensor network (WSN). Using self-organization, the sensor nodes all connect to one another to form a wireless network. Sensor gadgets are thought to be extremely difficult to recharge in unfavourable conditions. Moreover, network longevity, coverage area, scheduling, and data aggregation are the major issues of WSNs. Furthermore, the ability to extend the life of the network, as well as the dependability and scalability of sensor nodes' data transmissions, demonstrate the success of data aggregation. As a result, clustering methods are thought to be ideal for making the most efficient use of resources while also requiring less energy. All sensor nodes in a cluster communicate with each other via a cluster head (CH) node. Any clustering algorithm's primary responsibility in these situations is to select the ideal CH for solving the variety of limitations, such as minimising energy consumption and delay. Kinetic Gas Molecule Optimization (KGMO) is used in this paper to create a new model for selecting CH to improve network lifetime and energy. Gas molecule agents move through a search space in pursuit of an optimal solution while considering characteristics like energy, distance, and delay as objective functions. On average, the KGMO algorithm results in a 20% increase in network life expectancy and a 19.84% increase in energy stability compared to the traditional technique Bacterial Foraging Optimization Algorithm (BFO).

Enhancing Controller Efficiency in Hybrid Power System Using Interval Type 3 Fuzzy Controller with Bacterial Foraging Optimization Algorithm

Microgrids (MGs) are designed with the help of effective power extracted from renewable sources such as rooftop solar panels, photovoltaic cells, batteries, floating PV and solar PV with the grid. In a hybrid microgrid, Interlinking Converter (ILC) is a key component to connect the AC sub-grid and DC sub-grid. DC-DC converters are being used as power converters in between load and source to enforce and increase the PV depending on the voltage output signal. Accordingly, the work focused on a Multi-Input (MI) KY boost converter. This Proposed topology gathered maximum power using multi-input KY boost converters for hybrid energy. This hybrid topology operates mainly delivered power from renewable energy sources solar/wind to dc bus. In the absence of any one source, wind or solar supplies power to the dc bus. Without any renewable energy, sources battery deliver the power to the dc bus. The research proposed the interval type 3 fuzzy controller is used for controlling the load frequency of the multi-area system. Swarm-based hybrid metaheuristic optimizer of the Bacterial Foraging Optimization Algorithm (BFOA) is proposed for optimal tuning and controlling the PI controller parameters. Controlling the reactive power of the hybrid power system model with the aid of a Static Synchronous Compensator (STATCOM). A unique controller is deployed to regulate the AC and DC currents of the STATCOM using two PI controllers. In this paper effectiveness of the hybrid power system is simulated through MATLAB/SIMULINK. The battery current and voltage of this produce 2000 A and 205 V, grid voltage produced in this work is 1.9 × 104 V and the power of the work produce approximately 90 kW. The results show that the interlink converter improves the flexibility of the hybrid microgrid and, in addition, the power quality of the energy supplied in the utility grid is improved. In future, an intelligent control algorithm may be presented to improve the control strategies of the HMG, respectively.

Optimal gait planning of bionic lower extremity exoskeleton based on elite mechanism bacterial foraging optimization algorithm

To improve the stability and smoothness of bionic lower extremity exoskeleton walking gait, the elitist mechanism was introduced to enhance the diversity and improve the search ability of the optimal solution. The simulation analyses showed that the improved bacterial foraging algorithm had higher convergence accuracy, and was not easy to fall into local extremum. The improved foraging optimization algorithm was used for the optimization of the bionic lower extremity exoskeleton gait zero moment point stability margin. The simulation results showed that the optimal gait planning method proposed can increase the stability margin and ensure the stable and smooth walking.