Artificial Neural Network Controller Research Articles

Design and Performance Analysis of Level Control Strategies in a Nonlinear Spherical Tank

This work seeks to contribute to the study of techniques for level control considering a nonlinear plant model. To achieve this goal, different approaches are applied to classical control techniques and their results are analyzed. Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Model Predictive Control (MPC) and Nonlinear Auto-Regressive Moving Average (NARMA-L2) controllers are designed for the level control of a spherical tank. Subsequently, several tests and scenarios similar to those present in industrial processes are established, while the transient response of the controllers, their performance indices for monitoring the reference value, the rejection of disturbances, the presence of parameter uncertainties and the effects of noise are analyzed. The results show good reference tracking, with a settling time of approximately 5 s for 5 cm and a rise time of less than 4 s. No evidence for steady-state error or overshoot was found and controllers behave positively in the diverse scenarios assessed. The FLC and ANN controllers showed the greatest limitations, while ANFIS, MPC and NARMA-L2 exhibited competitive results considering their transient response and the performance indices calculated.

Load Flow Analysis and the Impact of a Solar PV Generator in a Radial Distribution Network

The distribution system acts as a conduit between the consumer and the bulk power grid. Due to characteristics such as a high resistance/reactance ratio, distribution networks cannot be solved using conventional methods, such as the Gauss-Seidel and Newton–Raphson. This research proposes a method for the calculation of the power flow in radial networks that considers their wide range of resistance and reactance values, PV generator characteristics, and radial structure. An iterative methodology is employed, with each iteration beginning with the branch that has the highest accurate power flow solution. The procedure is reliable and effective over various workloads and network configurations. To confirm the effectiveness of the suggested technique on the simple and IEEE 33-bus radial distribution system, simulations were carried out in MATLAB. The implications of including a renewable energy source, such as a PV generator, in the network under consideration are investigated by simulation result comparison. The optimal location of the PV generator was also determined using an Artificial Neural Network (ANN) controller. The results of the identification process improve the already exceptional efficacy and performance of the ANN controller.

Design of UPQC with Solar PV and Battery Storage Systems for Power Quality Improvement

In this paper, the unified PQ conditioner (UPQC) is associated with photovoltaic (PV) and battery-storage systems (BSSs) to address the PQ issues. The hybrid fuzzy-sliding mode control (HFSMC) based maximum-power-point tracking system (MPPTs) is adopted for solar PV system to extract maximum output. To minimize the complexity of the conventional system, levenberg-marquardt trained artificial neural network (ANN) controller is proposed to the UPQC in order to generate the effective reference signals for shunt, series converters which eliminates the necessity of conventional abc, dq0, αβ transformations. The main aim of the proposed ANN based UPQC is to achieve the constant direct current (DC) link capacitor voltage during load changes with low settling time, elimination of imperfections in current waveforms, thereby reducing total harmonic distortions (THD) and to improve power factor, mitigation of sag, swell, disturbances and unbalances in source voltage. The performance of developed method is tested on five case studies with different combination of loads, variable irradiation and source voltages conditions. To demonstrate the supremacy of the developed method the comparative study with the proportional integral controller (PIC) was carried out. The proposed method shows an excellent performance in reducing voltage fluctuations and THD successfully and thereby improving power factor.

Hybrid sliding neural network controller of a direct driven vertical axis wind turbine

This study aims to propose a robust hybrid sliding mode artificial neural network control (SM-ANN) scheme for controlling the stator power (active/reactive) of a doubly fed induction generator (DFIG)-based direct drive vertical axis wind turbine (VAWT) power system under a real-world scenario wind speed that will be installed in the Adrar region (Saharan zone) of Algeria. The SM-ANN scheme will control the stator power of the direct drive VAWT power. The chattering phenomenon is the most significant disadvantage associated with sliding mode control (SMC). In order to find a solution to this issue, the artificial neural network (ANN) method was applied to pick the appealing part of the SMC. MATLAB/Simulink is used to do an evaluation, after which the SM-ANN controller being suggested is compared to both traditional sliding mode (SM) and proportional-integral (PI) controllers. The results of the simulation demonstrated that the recommended SM-ANN controller has good performance in terms of enhancing the quality of energy that is delivered to the power network. This is in comparison to the traditional SM and PI controllers, which both have a long history of use. Notwithstanding the fact that there is DFIG parameter fluctuation present.

Design and Experimental Characterization of Artificial Neural Network Controller for a Lower Limb Robotic Exoskeleton

This study aims to develop a lower limb robotic exoskeleton with the use of artificial neural networks for the purpose of rehabilitation. First, the PID control with iterative learning controller is used to test the proposed lower limb robotic exoskeleton robot (LLRER). Although the hip part using the flat brushless DC motors actuation has good tracking results, the knee part using the pneumatic actuated muscle (PAM) actuation cannot perform very well. Second, to compensate this nonlinearity of PAM actuation, the artificial neural network (ANN) feedforward control based on the inverse model trained in advance are used to compensate the nonlinearity of the PAM. Third, a particle swarm optimization (PSO) is used to optimize the PID parameters based on the ANN-feedforward architecture. The developed controller can complete the tracking of one gait cycle within 3.6 s for the knee joint. Among the three controllers, the controller of the ANN-feedforward with PID control (PSO tuned) performs the best, even when the LLRER is worn by the user and the tracking performance is still very good. The average Mean Absolute Error (MAE) of the left knee joint is 1.658 degrees and the average MAE of the right knee joint is 1.392 degrees. In the rehabilitation tests, the controller of ANN-feedforward with PID control is found to be suitable and its versatility for different walking gaits is verified during human tests. The establishment of its inverse model does not need to use complex mathematical formulas and parameters for modeling. Moreover, this study introduces the PSO to search for the optimal parameters of the PID. The architecture diagram and the control signal given by the ANN compensation with the PID control can reduce the error very well.

Comparison of the characteristics of the control strategies based on artificial neural network and genetic algorithm for air conditioning systems

Air conditioning systems are playing an increasingly important role in our daily life, and the control of air conditioning systems is related to the intelligence of the system, indoor thermal comfort and energy consumption. Therefore, the control of air conditioning systems has always been an important research topic. Artificial neural network (ANN) and genetic algorithm (GA) are two commonly used intelligent control methods. However, there is no guidance until now on the applicability of these two control methods and how to make a choice between them in the control of air conditioning systems. In this study, an ANN control strategy and a GA control strategy were developed and experimentally verified. The experimental results indicated that both the ANN control strategy and the GA control strategy could control the air conditioning system properly under different control commands. To compare the control performances, the convergence speed, discrete characteristic, energy consumption and interference resistance ability were calculated or experimentally validated. The ANN control strategy showed better performances in the convergence speed and energy consumption. While the GA control strategy performed better in maintaining the stable state of the air conditioning system.The innovation of this study lies in two points. First, when designing the ANN control strategy and the GA control strategy, ANN and GA were applied as the central control algorithm, rather than only as auxiliary algorithms for system identification, prediction or optimization. This methodology is relatively novel in the design of ANN and GA control strategies for the air conditioning system, and could further expand the application of ANN and GA. Second, this study employed 4 evaluating indicators including convergence speed, discrete characteristic, energy consumption and interference resistance ability, to comprehensively evaluated the control performance of ANN control strategy and GA control strategy.

Smart Energy Consumption Control in Commercial Buildings Using Machine Learning and IOT

In recent years, the use of artificial intelligence (AI) techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), in combination with the Internet of Things (IoT), has gained significant attention for optimizing energy consumption in commercial buildings. With the increasing demand for energy and the rising costs of energy, there is a pressing need for efficient methods for energy management in commercial buildings. Smart energy consumption control systems that utilize machine learning algorithms and IoT devices can provide real-time data on energy usage and automate energy usage decisions in commercial buildings. In this paper, we investigate the potential of ANN and SVM-based smart energy consumption control systems in commercial buildings. We aim to analyze the impact of using these algorithms on energy consumption patterns in commercial buildings and evaluate the efficiency and effectiveness of these systems in reducing energy consumption and costs while maintaining the desired level of comfort for the occupants. Our study will focus on comparing the performance of ANN and SVM-based algorithms in terms of energy consumption reduction and cost savings. The results of this study can provide valuable insights into the application of ANN and SVM-based smart energy consumption control systems in commercial buildings and contribute to the development of more sustainable and energy-efficient buildings.

Learning Reactive Power Control Polices in Distribution Networks Using Conditional Value-at-Risk and Artificial Neural Networks

Scalable coordination of photovoltaic (PV) inverters, considering the uncertainty in PV and load in distribution networks (DNs), is challenging due to the lack of real-time communications. Decentralized PV inverter setpoints can be achieved to address this issue by capitalizing on the abundance of data from smart utility meters and the scalable architecture of artificial neural networks (ANNs). To this end, we first use an offline, centralized data-driven conservative convex approximation of chance-constrained optimal power flow (CVaR-OPF) in which conditional value-at-risk (CVaR) is used to compute reactive power setpoints of PV inverter, taking into account PV and load uncertainties in DNs. Following that, an artificial neural network (ANN) controller is trained for each PV inverter to emulate the optimal behavior of the centralized control set- points of PV inverter in a decentralized fashion. Additionally, the voltage regulation performance of the developed ANN controllers is compared with other decentralized designs (local controllers) developed using model-based learning (regression-based controller), optimization (affine feedback controller), and case-based learning (mapping) approaches. Numerical tests using real-world feeders corroborate the effectiveness of ANN controllers in voltage regulation and loss minimization.

Performance Analysis of Artificial Intelligence Controller for PV and Battery Connected UPQC

Nowadays, integration of the non-conventional energy sources like wind, tidal, solar etc into the grid is suggested in order to minimize the losses in the distribution network and to meet the demand. The arrival of the power electronics equipments to control the nonlinear loads has made an impact on the power quality. The unified power quality conditioner (UPQC) is a FACTS device with the back to back converters, coupled together with a DC-Link capacitor. This paper suggests an intelligent hybrid controller for the solar Photo-voltaic system and Battery storage system integrated UPQC. The proposed controller adapts both the qualities of artificial neural network and Integral sliding-mode controller. The synchronization of phases is created by self tuning filter (STF) in association with unit vector generation method (STF-UVGM) for the superior performance of UPQC during the unbalanced/ distorted supply voltages conditions. Therefore, the necessity of Phase-locked-loop, Low pass filters and High pass filters are eliminated. However, STF is used for separating the Harmonic and Fundamental components. In addition, STF-UVGM was used for generation of synchronization phases of series and shunt filters. The prime objectives of the suggested artificial neural network integral sliding mode hybrid controller (ANNISMHC) are fast action to retain the DC-Link voltage to the constant value during load/ irradiation variations, diminish the harmonics in the current waveforms, power-factor enhancement, maximum mitigation of sag, swell and disturbances in supply voltage, and compensation for the unbalanced supply voltages. The working of suggested ANNISMHC was investigated on five test cases for several combinations of loads, and balanced/unbalanced supply voltages. However, to demonstrate supremacy of the suggested ANNISMHC comparative study with the different controllers those are available in literature and also with the standard controllers like PIC, SMC, and FLC. The ANNISMHC shows an extra-ordinary performance in diminishing THD thereby improving PF and reducing voltage distortions.

Comparative Analysis between Proportional-Integral and Artificial Neural Network Control of a Grid-Connected PV System

This article presents a comprehensive analysis of the modeling and control techniques applied to a photovoltaic (PV) system that is connected to a three-phase grid. To successfully integrate the PV system with the electrical grid, an innovative and reliable controller has been designed and put into practice. The utilization of an artificial neural network (ANN) enables the system to optimize power extraction from the PV panels, benefiting from the ANN's resilience and swift response to varying conditions. Moreover, a robust proportional-integral (PI) control strategy is introduced to govern the grid-side operations. This strategy of action focuses on managing the injection of both active and reactive electricity into the grid as well as controlling the voltage of the DC bus. A series of detailed simulations were carried out evaluating the efficiency of the suggested control strategy in the MATLAB/SIMULINK. The results obtained from these simulations share insightful information on the effectiveness and efficiency of the control system in ensuring optimal operation and power management of the PV system within the grid-connected setup.

PSO Trained Feed Forward Neural Network Based SAPF for Power Quality Enhancement in Distribution Networks

Power distribution infrastructure is being harmed by the advent of nonlinear devices, which cause harmonics to enter into the power system networks and distort the voltage and current signals. Shunt Active Power Filter (SAPF) is a new power electronics-based technology that can reduce harmonics and improve the power quality in distribution networks. This research provides an efficient and inexpensive strategy to minimizing harmonics and improving the power quality in power distribution networks by employing Shunt Active Power Filter’s (SAPF), which uses the Particle Swarm Optimized Artificial Neural Network Controller (PSO-ANN). The goal of the PSO-ANN algorithms have been developed for SAPF is to improve system performance by lowering the amount of Total Harmonic Distortion (THD). In this work, the standard PI controller is initially tuned using the PSO algorithm to obtain the optimal gain values (Ki, Kp) for the PI controller. After that, these values of the PSO-PI controller's input and output will serve as a dataset for the ANN controller. Now, the PSO algorithm is being used to tune this ANN controller in order to acquire the optimal values for the weight and bias. Using the MATLAB/SIMULINK tool, the proposed algorithm's performance is evaluated and compared to that of a PSO-PI based SAPF and the conventional PI based SAPF. The results of the simulation demonstrate that a SAPF which is based on a PSO-ANN controller is capable of achieving superior THD in the drawing source current while maintaining minimum levels and which are acceptable in accordance with the IEEE-519 standard for harmonics.

An Advanced Artificial Neural Network Energy Management in Standalone PV Systems

With the ever-increasing prevalent power crisis and pollution of the environment, solar power, has attracted greater attention as a new and clean energy source. It provides an alternative solution for isolated sites with an unavailable grid connection. However, it is not without any drawbacks, mainly its intermittent nature, related primarily owing to its reliance on meteorological variables such as the temperature outside and the amount of sunlight. In effect, the PV systems that produced electrical energy could well display an electricity excess or deficit at the loads level, likely to result in system service discontinuity. In this respect, the present paper is designed to provide an intelligent management strategy to PV station owners with a dump load. It can involve serving two customers simultaneously according to the following scenarios: the PV production installation of the customer1 is greater than their required load; however, the customer2's neighboring station does not have enough power to cover its electrical load. This case brings electrical energy from the initial station to make up for the shortfall, and vice versa. Lithium-ion batteries step in the case when the essential electrical power cannot be delivered either by the local station or the neighboring one or to keep the accumulated power excess. If one of the stations (1 or 2) detects a power surplus and the batteries are completely charged, the generated power excess must be redirected to a secondary load, commonly known as the dump load. Relying on the artificial neural network controller, the suggested exchange control is used for two independent PV-battery stations with dump load. The MATLAB/Simulink attained simulation turns out to demonstrate the advanced controller’s noticeable performance and effectiveness in managing the standalone PV system’s operability in terms of continuous electrical energy delivery flow to the resistive load while reducing power waste and increasing the lithium-ion battery lifespan.

Intelligent Control for Voltage Regulation in the Distribution Network Equipped with PV Farm

A combined PSO-ANN control is proposed in this work to achieve the best voltage regulation in a distribution network, based on quick response and minimum average voltage deviation. The Jordanian Sabha Distribution Network (JSDN) with PV Farms is used as a real case study to examine a voltage variation issue. Two STATCOMs are used to solve the voltage fluctuation problem on the network’s three buses. The required reactive powers of STATCOMs for voltage regulation during load variation are calculated in offline mode using a particle swarm optimization (PSO) algorithm. Despite its high performance in solving voltage issue in the JSDN network, the PSO controller is unable to react promptly to dynamic changes in the network. An artificial neural network (ANN) is therefore suggested as an online mode controller for quick and efficient voltage regulation. The offline dataset is used to train the ANN for online voltage regulation utilizing the MATLAB-Tool Box. At an average voltage deviation (AVD) of 1.168%; (whereas an acceptable one is 6%), the results revealed the proposed ANN controller’s competence for voltage regulation in the distribution network. Moreover, to find the best position based on an efficient voltage regulation, many sites for STATCOMs are taken into consideration.

Model reference control by recurrent neural network built with paraconsistent neurons for trajectory tracking of a rotary inverted pendulum

This investigation presents a recurrent paraconsistent neural network (RPNN), as the main element of the model reference control (MRC) strategy for the rotary inverted pendulum (RIP). The RIP characteristics, such as nonlinearity, two-degree-of-freedom (2DoF) motion, and under-actuated system, make it an ideal device to apply and test the RPNN. The designed paraconsistent neural model reference controller (PNMRC) uses three RPNNs: two of them to model the arm and pendulum angles and the third one to control the system while tracking a reference trajectory. The hidden neurons of the RPNN use the paraconsistent annotated logic by 2-value annotations (PAL2v) rules as an activation function. PAL2v, as a member of the paraconsistent logics family, deals with uncertain and contradictory data, representing a potentially robust alternative to applications of artificial neural networks in control. The PAL2v neuron is detailed and compared with other activation functions in recurrent neural networks (RNN). With real-time experiments, the PNMRC strategy is compared with classical control methodology, presenting excellent performance.

Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

Abstract Photovoltaic (PVS) generators’ nonlinear electrical characteristics allow for greater performance and efficiency when they are forced to operate at their peak power (MPP). This article suggests an adaptive method for maximizing power point tracking that makes use of artificial neural network (ANN) techniques (MPPT). A step-up converter powered by a separate solar generator is under the control of an ANN controller built on a neural network training database (PVS). The results show that ANN-MPPT has good control performance and is near to the maximum power point of PVS when compared to conventional MPPT methods like perturb and observe and incremental conductance.

Design and analysis of Hermite function‐based artificial neural network controller for performance enhancement of photovoltaic‐integrated grid system

AbstractThis article proposes a Hermite function‐based artificial neural network (HeANN) controller for shunt compensation in photovoltaic (PV) interfaced grid system. Various power quality (PQ) issues such as load change, poor power factor, reactive power demand and current harmonics are experienced by distribution system. The proposed HeANN algorithm is utilized to improve the PQ issues of the distribution system. Shunt active power filter (SAPF) is operated in current control mode and injects the compensating current at the point of common coupling (PCC). The inverter is connected with the green energy resource, that is, PV at its direct current terminals and contributes its power to fulfil the load active power demand. The parameters of HeANN are trained in real time. A prototype hardware of SAPF is developed using sensor circuits and space 1104. The simulation results are validated by experimental results. The comparative analysis of the results establishes the superiority of the proposed control technique to other control techniques such as synchronous reference frame, second order generalized integrator and back propagation neural network in terms of convergence, computational burden and total harmonic distortion of grid current.

A Smart ANN-Based Converter for Efficient Bidirectional Power Flow in Hybrid Electric Vehicles

Electric vehicles (EV) are promising alternate fuel technologies to curtail vehicular emissions. A modeling framework in a hybrid electric vehicle system with a joint analysis of EV in powering and regenerative braking mode is introduced. Bidirectional DC–DC converters (BDC) are important for widespread voltage matching and effective for recovery of feedback energy. BDC connects the first voltage source (FVS) and second voltage source (SVS), and a DC-bus voltage at various levels is implemented. The main objectives of this work are coordinated control of the DC energy sources of various voltage levels, independent power flow between both the energy sources, and regulation of current flow from the DC-bus to the voltage sources. Optimization of the feedback control in the converter circuit of HEV is designed using an artificial neural network (ANN). Applicability of the EV in bidirectional power flow management is demonstrated. Furthermore, the dual-source low-voltage buck/boost mode enables independent power flow management between the two sources—FVS and SVS. In both modes of operation of the converter, drive performance with an ANN is compared with a conventional proportional–integral control. Simulations executed in MATLAB/Simulink demonstrate low steady-state error, peak overshoot, and settling time with the ANN controller.

Optimal Design of an Artificial Intelligence Controller for Solar-Battery Integrated UPQC in Three Phase Distribution Networks

In order to minimize losses in the distribution network, integrating non-conventional energy sources such as wind, tidal, solar, and so on, into the grid has been proposed in many papers as a viable solution. Using electronic power equipment to control nonlinear loads impacts the quality of power. The unified power quality conditioner (UPQC) is a FACTS device with back-to-back converters that are coupled together with a DC-link capacitor. Conventional training algorithms used by ANNs, such as the Back Propagation and Levenberg–Marquardt algorithms, can become trapped in local optima, which motivates the use of ANNs trained by evolutionary algorithms. This work presents a hybrid controller, based on the soccer league algorithm, and trained by an artificial neural network controller (S-ANNC), for use in the shunt active power filter. This work also presents a fuzzy logic controller for use in the series active power filter of the UPQC that is associated with the solar photovoltaic system and battery storage system. The synchronization of phases is created using a self-tuning filter (STF), in association with the unit vector generation method (UVGM), for the superior performance of UPQC during unbalanced/distorted supply voltage conditions; therefore, the necessity of the phase-locked-loop, low-pass filters, and high-pass filters are totally eliminated. The STF is used for separating harmonic and fundamental components, in addition to generating the synchronization phases of series and shunt filters. The prime objective of the suggested S-ANNC is to minimize mean square error in order to achieve a fast action that will retain the DC-link voltage’s constant value during load/irradiation variations, suppress current harmonics and power–factor enhancement, mitigate sagging/swelling/disturbances in the supply voltage, and provide appropriate compensation for unbalanced supply voltages. The performance analysis of S-ANNC, using five test cases for several combinations of loads/supply voltages, demonstrates the supremacy of the suggested S-ANNC. Comparative analysis was carried out using the GA, PSO, and GWO training methods, in addition to other methods that exist in the literature. The S-ANNC showed an extra-ordinary performance in terms of diminishing total harmonic distortion (THD); thus PF was improved and voltage distortions were reduced.

Power Quality Improvement in a Solar PV Assisted Microgrid Using Upgraded ANN-Based Controller

This paper proposes the design of a controller using the artificial neural network (ANN) for a solar photovoltaic (PV)-fed cascaded multilevel inverter (CMLI) to enhance the power quality. The objective of this presented ANN controller is to obtain a maximum output voltage with no filter components. This paper also investigates and eliminates the voltage harmonics that occurred in a solar-fed cascaded 3-stage inverter using various techniques such as pulse width modulation (PWM), digital logic control (DLC), fuzzy logic controller (FLC), and ANN, and the results are compared. Based on the results, the proposed ANN-based controller efficiently reduces harmonics and improves the power quality. This is achieved by solving the harmonic equations and thereby changing the switching angles of each semiconductor to a minimum value. The ANN is trained by a dataset consisting of varying input voltage and switching angles. The simulation was performed using MATLAB/Simulink for different types of controllers like PWM, DLC, FLC, and ANN for the 3-stage inverter. The simulated results are compared with the results obtained from a 3 kWp photovoltaic plant connected to the CMLI. Finally, on the basis of performance analysis, it was confirmed that the ANN-based controller effectively eliminates harmonics and improves the power quality.

An optimal artificial neural network controller for load frequency control of a four-area interconnected power system

In this paper, an optimal artificial neural network (ANN) controller for load frequency control (LFC) of a four-area interconnected power system with non-linearity is presented. A feed forward neural network with multi-layers and Bayesian regularization backpropagation (BRB) training function is used. This controller is designed on the basis of optimal control theory to overcome the problem of load frequency control as load changes in the power system. The system comprised of transfer function models of twothermal units, one nuclear unit and one hydro unit. The controller model is developed by considering generation rate constraint (GRC) of different units as a non-linearity. The typical system parameters obtained from IEEE press power engineering series and EPRI books. The robustness, effectiveness, and performance of the proposed optimal ANN controller for a step load change and random load change in the system is simulated through using MATLAB-Simulink. The time response characteristics are compared with that obtained from the proportional, integral and derivative (PID) controller and non-linear autoregressive-moving average (NARMA-L2) controller. The results show that the algorithm developed for proposed controller has a superiority in accuracy as compared to other two controllers.