Artificial Neural Network Controller Research Articles

ANN-fuzzy Hybrid Control Strategy for MPPT of Grid-connected PV System with Battery Storage under Fast-changing Atmospheric Condition

Background: The use of solar energy through photovoltaic arrays is continually expanding and has recently been regarded as one of the cleanest sources of energy. Increasing the amount of electricity provided to the load is one method for lowering the cost of solar systems. Contrarily, altering the load creates a divergence from the maximum power point (MPP) and changes the operating point of the solar conversion system. Methods: Due to this, attention has been given to MPPT techniques that can be used with solar systems in numerous research investigations. In this paper, we implement an MPPT method based on an Artificial Neural Network (ANN). This method combined two controllers, ANN and fuzzy logic controller, under the name ANN-Fuzzy logic hybrid. In the first stage, ANN can determine Vmpp from irradiation and temperature, and both of them are variable. In the second stage, this Vmpp has been corrected by implementing Fuzzy logic in order to minimize the error of the voltage and VmPP. Results: ANN-fuzzy hybrid has been simulated in Matlab-Simulink and was found to be the best solution to follow the maximum power point when irradiation and temperature are varied. The energy extracted from PV is delivered to a battery in order to inject this power into the smart grid by using an inverter controlled by PID. Finally, a LC filter has been used to eliminate the harmonics and compensate for the reactive power. Conclusion: For energy storage, we consider the utilization of lithium-ion batteries, which are recognized as an optimal solution for storing energy efficiently. To manage the charge and discharge of the battery, we employ a PID controller and a buck-boost converter. Through this research, we aim to explore the performance and effectiveness of the hybrid ANN-fuzzy simulation for the boost converter system. By combining the capabilities of ANN and fuzzy logic, we expect to achieve improved control and optimization of the power conversion processes in the system. Additionally, the integration of lithium-ion batteries and the use of the PID controller and buck-boost converter allow for efficient management of energy storage and retrieval. Overall, this study investigates the hybrid ANN-fuzzy simulation for the boost converter system, highlighting the integration of various components and control techniques to enhance the performance and efficiency of the system.

Mobility Deficit Identification and Compensation through an Artificial Neural Network and Adaptive Controller Design during Gait

Mobility Deficit Identification and Compensation through an Artificial Neural Network and Adaptive Controller Design during Gait

IoT enabled microgrid framework using a novel dispersal diffusion artificial neural network controller for PV systems and wind energy to minimize electrical faults

IoT enabled microgrid framework using a novel dispersal diffusion artificial neural network controller for PV systems and wind energy to minimize electrical faults

Power Quality Improvement of PV fed Grid Connected System using ANN Controlled Shunt Active Power Filter

Harmonics are common in integrated power systems, especially with the increasing use of nonlinear loads (NLL), such as those found in photo voltaic (PV) systems connected to the grid. Traditional LC filters Shunt active power filters (SAPF) have been developed to effectively correct harmonics and improve power quality performance. This study presents a three-phase voltage-fed SAPF implementation to mitigate harmonics using an artificial neural network (ANN) controller. The SAPF control system focuses on generating reference source currents to counterbalance the harmonic effects caused by NLL. The model's effectiveness is validated using experimental data gathered from a nonlinear load through MATLAB/Simulink simulations.

Performance Analysis of DC Motors With Integrated Proportional-Integral and Artificial Neural Network Control

Direct current (DC) motors are frequently utilized in various applications, and the motor's pace is affected by applied loads as it fluctuates. A power converter must be employed to control the velocity of the motor by varying the armature voltage. One of the options for the power converter is the one-quadrant DC chopper. In this case, the investigation will turn the one-quadrant chopper into a system by merging velocity and current control into the DC motor. The speed is regulated by controlling the armature voltage. This may be accomplished using a controlled rectifier. The contribution of the research is to test the effectiveness of Artificial Neural Network Control (ANN) and Proportional-Integral (PI) controllers to control the speed of a DC motor using a one-quadrant DC chopper. Therefore, due to technological advancements, the authors will utilize the training data of the artificial neural network of Proportional- Integral controllers in MATLAB's Simulink. Test results demonstrate the artificial neural network (ANN's) superior ability to regulate system response, showing enhancements in delay time, rise time, overshoot, and steady-state error compared to the PI controller. These findings underscore the potential of ANN as a more sophisticated choice for DC motor control, although further research is required to finetune its performance through rigorous training.

Analysis and control of hybrid convection-radiation drying systems toward energy saving strategy.

Food drying is a vital technology in agricultural preservation, where hybrid drying provides efficient moisture removal. However, the transient behavior of the moisture ratio ( ) and associated drying rate ( ) could increase energy consumption if not managed with an effective energy-saving strategy. This study investigates the impacts of hybrid convection-radiation drying on , , drying time, and energy consumption. Unlike previous studies, which have not fully addressed the connections between operating conditions and drying kinetics, this work provides new insights into these relationships. Additionally, an artificial neural network (ANN) control model is applied to optimize energy consumption, offering a new approach to improving the efficiency of the drying process. During the experiment, the airflow velocity was varied from 0.7m/s to 1.5m/s, the airflow temperature was varied from 40°C to 60°C, and the radiation intensity was varied from 1500W/m2 to 3000W/m2. The results showed that the transient behavior exhibited four data groups with consistent and through the mean and statistical analysis. Increasing radiation intensity and air temperature has decreased the drying time, while higher airflow has increased the drying time. The energy indices were enhanced by increasing radiation intensity and temperature while reducing airflow velocity. The measured of all groups exhibited similar kinetics behavior, while the associated exhibited similar clustering through the self-organizing map. Those findings were further controlled using an ANN model with 99% predicting accuracy. With airborne heating at 60°C and airflow at 0.7m/s, the radiation intensity is transiently controlled, showing a 6.5% drying time reduction and a 36% energy saving. Thus, controlling the radiation intensity and its impact on the slice properties is highly desirable in future work. This work could help designers improve the processing efficiency and energy conservation of garlic slices drying.

Improvement of the Transient Levitation Response of a Magnetic Levitation System Using Hybrid Fuzzy and Artificial Neural Network Control

Improvement of the Transient Levitation Response of a Magnetic Levitation System Using Hybrid Fuzzy and Artificial Neural Network Control

Enhancing Multi-Machine Power System Stability with STATCOM-SMES: A Soft Computing Approach

The increasing complexity and increasing mismatch between supply and demand within modern power systems necessitate advanced methods for ensuring system stability and reliable operation. This article investigates the enhancement of transient stability in a multi-machine power system through the integration of Flexible AC Transmission Systems (FACTS) devices, specifically a Static Synchronous Compensator (STATCOM) combined with energy storage. Leveraging soft computing techniques such as Fuzzy Logic Controllers (FLC) and Artificial Neural Networks (ANN), this study evaluates the performance improvements in transient stability. The methodology involves modeling a multi-machine power system, implementing STATCOM-SMES configurations, and designing FLC and ANN controllers to dynamically support system stability. Simulations are conducted using MATLAB/Simulink, applying a three-phase to ground fault to assess the system's transient response. Performance metrics such as peak overshoot, settling time, and damping ratio are analyzed to compare the effectiveness of the proposed techniques. The results demonstrate significant improvements in transient stability with the application of FLC and ANN controlled STATCOM-SMES systems. This study underscores the potential of integrating soft computing techniques with FACTS devices to enhance the dynamic performance and resilience of power systems, contributing valuable insights for future research and practical applications in power system stability.

Analysis of Three Phase Induction Motor Settings To Load Changes Using Artificial Neural Networks

benefits of induction motors in the industrial world today are numerous. This is because this induction motor has many advantages such as strong and simple construction and is cheaper to maintain. However, there are weaknesses in regulating speed and torque. One of the 3-phase induction motor speed regulation is vector control. This method is connected to a PID controller to control the input and output to reach the expected value. However, with the development of an increasingly modern era, it can now be done automatically, especially in regulating PID control, in this experiment, it wants to be done using an artificial neural network controller to be able to study the data that has been done by the PID controller to find out the input and output expected by the system so that the speed control on the induction motor runs stably. this method is able to control the speed to remain stable even though the load changes. In this study, loading tests will be carried out at 0 Nm, 80 Nm, 140 Nm, 290 Nm and 400 Nm and reference speeds of 40 rps, 75 rps and 120 rps. The simulation results of using the artificial neural network controller show that the system can reach the expected reference speed in less than 3 ms and the average steady state error is 0.5241%.

A hybrid algorithm for photovoltaic maximum power point tracking using Artificial Neural Network and Kinetic Gas Molecular Optimization

The quest for efficient photovoltaic (PV) energy conversion systems has led to the development of Maximum Power Point Tracking (MPPT) algorithms. In this paper, we propose a novel hybrid approach that combines the power of Artificial Neural Network (ANN) with the optimization capability of Kinetic Gas Molecular Optimization (KGMO) for MPPT. We present a high-level outline of the proposed algorithm along with example MATLAB code snippets for each step, highlighting its potential for improving PV system performance. This paper proposes a metaheuristic optimized multilayer feed‐forward artificial neural network (ANN) controller to extract the maximum power from available solar energy. Firstly, to improve the maximum power point (MPP) delivered by PV arrays and to overcome the drawbacks in the conventional MPPT method under irradiation variation, a hybrid MPPT controller is designed, in which the input parameters include the PV array voltage and current. The output parameter is the duty cycle of the DC/DC boost converter. The proposed approach abbreviated as ANN-KGMO MPPT controller is based on the Kinetic Gas Molecular Optimization (KGMO) Algorithm which is useful to train the developed ANN and to evolve the connection weights and biases to get the optimal values of duty cycle converter corresponding to the MPP of a PV array. Finally, the performance of the proposed control system is confirmed by simulation tests on a 2 kW PV system. In addition, the performance of an ANN-KGMO -based MPPT controller is also compared to the conventional perturb and observe (P&O) method. To analyze the results, simulations are performed by using MATLAB software.

Intelligent Control Algorithms for Enhanced Frequency Stability in Single and Interconnected Power Systems

Ensuring stable power system performance is crucial for reliable grid operation. This study assesses various Load Frequency Control (LFC) strategies, including conventional PID, pole placement, Genetic Algorithm (GA)-optimized PID, Particle Swarm Optimization (PSO)-optimized PID, and an Artificial Neural Network (ANN)-based controller, in single and interconnected power grids. The results reveal that GA- and PSO-optimized PID outperform conventional methods, offering minimal overshoot and fast settling times. Pole placement strikes a balance between response time and stability, while the ANN controller demonstrates adaptability and quick rise times but exhibits higher overshoot and longer settling times compared to the optimization techniques. Tie-line bias control aids in frequency stabilization but presents challenges with overshoot and prolonged settling times. Notably, PSO-optimized PID emerges as a promising solution, effectively mitigating overshoot and achieving rapid frequency recovery. This study underscores the importance of tailored control strategies for optimal LFC, which are essential for enhancing power system stability and efficiency. Future research should explore the potential of advanced techniques, such as deep learning and reinforcement learning, to further improve control performance.

Artificial neural network based decentralized current-sharing control for parallel connected DC-DC converters in DC microgrid application

To create a non-interrupted, economical and reliable direct current (DC) microgrid, multiple DC-DC converters can be connected in parallel. Current sharing among these multiple converters becomes essential for proper operation of the system. This study proposes an artificial neural network (ANN) based control technique for parallel connected DC-DC boost converters which ensures accurate current sharing according to the specified maximum limits. Levenberg-Marquardt algorithm-based ANN network is used to reduce the training time and, also to achieve near ≈ 100 % accuracy of the training data. ANN control provides better voltage regulation, accurate current sharing unlike the conventional proportional-integral (PI) based control which faces issues such as inaccurate current sharing, high transient, peak overshoots and steady state error during sudden change in the system. The efficient functioning of the proposed control method is verified by simulating two parallel connected DC-DC converters using MATLAB/Simulink. A hardware prototype of converters rating ≈ 250 W using TMS320F28379D Digital signal processor controller is also developed to verify the performance and effectiveness of the ANN based control in comparison to PI based technique. The ANN based technique is faster to achieve the reference voltage, and the peak overshoot is approximately 75 % lesser than the PI based control.

Artificial neural network controller based on model predictive control

Artificial neural network controller based on model predictive control

Regulation of Active and Reactive Powers in Doubly-Fed Induction Generators Utilizing Proportional-Integral and Artificial Neural Network Controllers

Regulation of Active and Reactive Powers in Doubly-Fed Induction Generators Utilizing Proportional-Integral and Artificial Neural Network Controllers

Neural network-based self-tuning control for hybrid electric vehicle engines

Combustion engine speed control faces significant uncertainties in parameters like mass equivalent coefficient and efficiency. To address these challenges, this research develops an adaptive, self-tuning artificial neural network (ANN) control design that aims to optimize engine speed despite load torque and model uncertainties. We integrate three controllers—linear quadratic regulator (LQR), proportional-integral-derivative (PID), and sliding mode controller (SMC)—with machine learning techniques to evaluate their performance in hybrid electric vehicle engines. The ANN-based self-tuning controller identifies the most effective method for maintaining idle engine speed with minimal deviation. The results indicate that the ANN can accurately predict settling times and enhance controller performance, leading to reduced settling time errors, improved fuel economy, and better emissions performance. The study highlights LQR's high sensitivity in the lower settling time range and reduced sensitivity in the higher range, the PID controller's varied sensitivity, and SMC's consistent sensitivity across both ranges. A well-trained ANN is essential for optimizing control parameters and maintaining system stability. The ANN was trained and validated using the nntool MATLAB toolbox, with tests conducted across lower and higher settling time ranges. Results showed that the PID controller's performance was overfitted by insufficient training in the lower range, while the LQR and SMC demonstrated potential for more efficient control in both ranges. Overall, the study underscores the importance of a proficiently trained ANN for achieving precise control in hybrid electric vehicle engines.

Intelligent MPPT for High Gain Zeta Converter for Standalone PV Application with Galvanic Isolation

In this paper, Standalone PV system is designed to operate without the need of electric utility grid which are commonly designed to supply specific DC electric loads. These type of PV system are generally powered using photovoltaic array and consist of solar charging modules, controllers and regulators. The objective of this work is to implement the Radial Basis Function Neural Network (RBFNN) based Maximum Power Point Tracking (MPPT) technique to attain constant DC link voltage of the ZETA converter. This method utilizes a ZETA converter to achieve better voltage gain and lower ripple in the output current and voltage. Using an Artificial Neural Network (ANN) controller to operate a high frequency converter, which improves power conversion efficiency. To ensure safety and prevent electrical faults, an isolation transformer provides galvanic isolation between the high frequency converter and the PV system. Galvanic isolation avoids electrical faults from being transmitted and safeguards connected loads. The rectifier is used to supply the Direct Current (DC) load with the output from the isolation transformer. The rectifier changes the transformer’s Alternating Current (AC) output into DC current to power DC loads. An intelligent MPPT is employed to stabilize the converter’s output voltage and address the intermittent characteristics of PV, and also assists in sustaining the DC link voltage without alterations and ripples from the converter. The efficiency of this work is authenticated via MATLAB simulation 2021a.

Advanced Controller System with Hybrid Renewable Sources Enabling Vehicle-To-Grid and Grid-To-Vehicle Operations

Power demand is rising worldwide, forcing the search for an alternative source to solve the imminent power crisis, which is expected to be accomplished through the deployment of grid synchronized electric vehicles (EV). Wind and solar energy are incorporated into this integration to those concerns. One smart grid innovation that permits energy exchange between the EV and the grid is vehicle to grid (V2G) technology. For the purpose of charging EVs, this research proposes employing hybrid energy. Here, the neighboring household’s linear and non linear demands are powered by the excess electricity from the photovoltaics (PV) panel, wind turbine, and AC generator that is utilized to charge the EV battery. Because of its dependability and independence from the real system model, a proportional integral (PI) controller is used to maximize the DC voltage extracted from the PV panel. The improved DC voltage overall is a result of the SEPIC converter’s improved DC output. Concurrently, an AC generator is incorporated into the system to supply extra power to the grid. An artificial neural network (ANN) controller is used to convert the power generated by the PV panel’s Sepic conversion process into an improved output that is then converted to AC using a 3 phase Voltage Source Inverter (VSI). Harmonic components in the VSI output are corrected with an LC filter to ensure grid compatibility and safe current injection into the grid. This paper discusses the usage of a bidirectional converter to adjust the EV battery’s voltage. MATLAB 2021a / Simulink software is used to simulate and validate the proposed approach.

Optimization of solar energy using artificial neural network vs recurrent neural network controller with ultra lift Luo converter

Optimization of solar energy using artificial neural network vs recurrent neural network controller with ultra lift Luo converter

Optimization of solar energy using artificial neural network vs recurrent neural network controller with positive output super lift Luo converter

In today’s world, the need for clean energy is crucial. Historically, Renewable energy sources like hydropower, wind, and solar offer sustainable solutions. Photovoltaic (PV) systems convert sunlight into electricity using semiconductor PV cells, which have been efficient for over 30 years. PV cell efficiency depends on irradiance (solar photon intensity) and temperature. Higher irradiance increases efficiency, while higher temperatures decrease it. PV systems, despite low voltage outputs, can be optimized using DC-DC Positive Output Super Lift Luo converters to match load requirements, enhancing system efficiency. Solar irradiance varies throughout the day, affecting PV cell output. Maximum Power Point Trackers (MPPTs) adjust the system's operating point to maintain peak efficiency. This study focuses on designing AI controllers to manage MPPT. We compare the performance of Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN) using three datasets. The goal is to identify the most efficient AI controller for optimizing solar energy systems.

Optimization of solar energy using artificial neural network controller with dc-dc boost, cuk, and single-ended primary inductor converter (SEPIC) converters

Optimization of solar energy using artificial neural network controller with dc-dc boost, cuk, and single-ended primary inductor converter (SEPIC) converters