Search

This paper introduces a novel energy management framework, Deep-Fuzzy Logic Control (Deep-FLC), which combines predictive modelling using Long Short-Term Memory (LSTM) networks with adaptive fuzzy logic to optimise energy allocation, minimise grid dependency, and preserve battery health in grid-connected microgrid (MG) systems. Integrating LSTM-based predictions provides foresight into system parameters such as state of charge, load demand, and battery health, while fuzzy logic ensures real-time adaptive control. Results demonstrate that Deep-FLC achieves a 25.7% reduction in operational costs compared to the conventional system and a 17.5% saving cost over the Fuzzy Logic Control (FLC) system. Additionally, Deep-FLC delivers the highest battery efficiency of 61% and constraints depth of discharge to below 2% per time step, resulting in a reduction of the state of health degradation to less than 0.2% over 300 h. By combining predictive analytics with adaptive control, this study addresses the limitations of standalone approaches and establishes Deep-FLC as a robust, efficient, and sustainable energy management solution. Key novel contributions include the integration of advanced prediction mechanisms with fuzzy control and its application to battery-integrated grid-connected MG systems. [ABSTRACT FROM AUTHOR]

Nowadays, wind turbines are one of the most popular devices for producing clean and renewable electric energy. The rotor blades catch the wind's kinetic energy to produce rotational energy from the turbine and electric energy from the generator. In small-scale wind turbines, there are several methods to operate the blades to obtain the desired speed of rotation and power outputs. These methods include passive stall, active stall, and pitch control. Pitch control sets the angular position of the blades to face the wind to achieve a predefined relationship between turbine speed or power and wind velocity. Typically, conventional Proportional Integral (PI) controllers are used to set the angular position of the rotor blades or pitch angle. Nevertheless, the quality of speed or power regulation may vary substantially. This study introduces a rotor speed controller for a pitch-controlled small-scale wind turbine prototype based on fuzzy logic concepts. The basics of fuzzy systems required to implement this kind of controller are presented in detail to counteract the lack of such material in the technical literature. The knowledge base of the fuzzy speed controller is composed of Takagi–Sugeno–Kang (TSK) fuzzy inference rules that implement a dedicated PI controller for any desired interval of wind velocities. Each wind velocity interval is defined with a fuzzy set. Simulation experiments show that the TSK fuzzy PI speed controller can outperform the conventional PI controller in the speed and accuracy of response, stability, and robustness over the whole range of operation of the wind turbine prototype. [ABSTRACT FROM AUTHOR]

The heating source temperature of the vacuum belt system (VBD) is an important factor affecting the drying rate and the material quality. However, it has problems with large fluctuation, instability, and hysteresis due to interference from various factors, which increases the drying time and energy consumption. To address these issues, this study proposes fuzzy control and integral control synergistic (FCICS) control to realize temperature regulation of the VBD system, enhancing the performance and stability of the heating source. Simulations were conducted in Simulink, and an experimental verification was carried out based on the constructed experimental system. The results show that the FCICS control outperforms the conventional PID control in terms of material warming rate, temperature stability, and energy consumption, and the transient and stable state performance is improved. Specifically, the material warming rate increased by 15%, temperature stability improved by 20%, and energy consumption decreased by more than 1.74% with the FCICS control strategy. [ABSTRACT FROM AUTHOR]

In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle's performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system's power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply's potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles. [ABSTRACT FROM AUTHOR]

From the point of view of the synthesis of control influences, arc steelmaking furnaces are complex nonlinear objects with strongly expressed mutual influences. It has been demonstrated that at a given supply voltage, the distribution of the current values of the phase currents in the quasi-steady-state mode makes it possible to estimate the situation in the arc space of an arc steelmaking furnace and identify the value of arc lengths. This dependence is preserved in transient modes. In order to identify arc lengths from the phase currents, it is proposed to use an approach based on the theory of fuzzy sets. The construction of the fuzzy arc length identifier rule base was carried out in two stages: the first stage used data from quasi-steady-state modes and the Wang–Mendel algorithm; the second stage involved adding a new rule to the database if the activity level of the formed rules was lower than the established level α for the data obtained in the dynamic mode. Further optimization of the parameters of the fuzzy identifier for operation in dynamic modes was carried out using the "back-propagation" algorithm. Based on the identified values of arc lengths, a control system for the movement of electrodes in an arc steelmaking furnace was synthesised. The proposed control system makes it possible to eliminate unproductive electrode movements due to changes in the situation in other phases of the arc steelmaking furnace and simplifies the application of modern methods of synthesising a control system for such complex objects. The results obtained in the mathematical model have confirmed the effectiveness of the proposed control system for the movement of electrodes in an arc steelmaking furnace. [ABSTRACT FROM AUTHOR]

As the penetration rate of wind power systems is rising, which causes the overall system's inertia to decline, the power system's capacity to regulate frequency will be negatively affected. Therefore, this paper investigates the inertia control of doubly fed induction generation, and an energy storage system is installed in the wind farm to respond to the frequency deviation. First, a fuzzy control-based virtual inertia adaptive control strategy is presented. The goal of dynamic adjustment of the virtual inertia coefficient is realized by taking into account the uncertain factors of wind speed and frequency change rate. A recovery strategy based on the energy storage system's level of charge is employed to prevent overcharging and over-discharging of the battery. Then, a weight factor based on frequency deviation is introduced to combine the droop output of the energy storage system with the virtual inertia output of the doubly fed induction generation, and the joint output mode of the wind storage system is determined in each stage of primary frequency regulation. Finally, the simulation verification is performed using the wind storage system simulation model created by MATLAB. The comparison results with other control methods prove that the proposed method is effective. [ABSTRACT FROM AUTHOR]

This paper is the second part of the study of a planar manipulator and this section presents the construction of a prototype manipulator. A fuzzy control system for the manipulator is described in detail. An experimental study was carried out on the positioning of the end effector of the manipulator and a program written in the Delphi 6 environment was proposed to calculate the position. Prototype tests were performed for transpose and follow-up control. Based on the experimental results, a control quality analysis was carried out. [ABSTRACT FROM AUTHOR]

The EHA is a highly integrated closed hydraulic system which is widely used in aerospace, vehicles, and robotics because of its high power and lightweight size. Due to the high non-linearity of the control system, it is difficult to reach a high robustness, leading to instability of the EHA system. In this paper, the fuzzy PID control strategy is proposed for overall control of the whole EHA system. Firstly, the structure and operating principle of EHA are analyzed. Secondly, the mathematical model and simulation model of the EHA-FPVM were established. Specially, to solve the problem of difficult calculation of non-linear factors such as friction and external forces, a co-simulation model was built in Matlab/Simulink AMESim, and a fuzzy PID controller was designed to control the EHA-FPVM. Finally, the PID and fuzzy PID controller were used to conduct simulation experiments, the simulation results are compared, and a servo-hydraulic system evaluation method is introduced to assess the simulation results. The results show that the EHA fuzzy PID control system has better output performance, lower overshoot percentages and steady-state errors, and the obtained evaluation scores are higher and more suitable for controlling EHA. [ABSTRACT FROM AUTHOR]

In this paper, we explain the design and implementation of an intelligent irrigation control system based on fuzzy logic for the automatic control of water pumps used in farms and greenhouses. This system enables its user to save water and electricity and prevent over-watering and under-watering of the crop by taking into account the climatic parameters and soil moisture. The irrigation system works without human intervention. The climate sensors are packaged using electronic circuits, and the whole is interfaced with an Arduino and a Simulink model. These sensors provide information that is used by the Simulink model to control the water pump speed; the speed of the water pump is controlled to increase or decrease the amount of water that needs to be pushed by the pump. The Simulink model contains the fuzzy control logic that manages the data read by the Arduino through sensors and sends the command to change the pump speed to the Arduino by considering all the sensor data. The need for human intervention is eliminated by using this system and a more successful crop is produced by supplying the right amount of water to the crop when it is needed. The water supply is stopped when a sufficient amount of moisture is present in the soil and it is started as soon as the soil moisture levels drops below certain levels, depending upon the environmental factors. [ABSTRACT FROM AUTHOR]

The increased use of distributed energy resources, especially electrical energy storage systems (EESS), has led to greater flexibility and complexity in power grids, which has led to new challenges in the operation of these systems, with particular emphasis on frequency regulation. To this end, the transmission system operator in Great Britain has designed a control scheme known as Enhanced Frequency Response (EFR) that is especially attractive for its implementation in EESS. This paper proposes a Type-2 fuzzy control system that enables the provision of EFR service from a battery energy storage system in order to improve the state-of-charge (SoC) management while providing EFR service from operating scenarios during working and off-duty days. The performance of the proposed controller is compared with a conventional FLC and PID controllers with similar features. The results showed that in all scenarios, but especially under large frequency deviations, the proposed controller presents a better SoC management in comparison without neglecting the EFR service provision. [ABSTRACT FROM AUTHOR]

With the increasing penetration of wind power and demand response integrated into the grid, the combined uncertainties from wind power and demand response have been a challenging concern for system operators. It is necessary to develop an approach to accommodate the combined uncertainties in the source side and load side. In this paper, the fuzzy stochastic conditional value-at-risk criterions are proposed as the risk measure of the combination of both wind power uncertainty and demand response uncertainty. To improve the computational tractability without sacrificing the accuracy, the fuzzy stochastic chance-constrained goal programming is proposed to transfer the fuzzy stochastic conditional value-at-risk to a deterministic equivalent. The operational risk of forecast error under fuzzy stochastic conditional value-at-risk assessment is represented by the shortage of reserve resource, which can be further divided into the load-shedding risk and the wind curtailment risk. To identify different priority levels for the different objective functions, the three-stage day-ahead unit commitment model is proposed through preemptive goal programming, in which the reliability requirement has the priority over the economic operation. Finally, a case simulation is performed on the IEEE 39-bus system to verify the effectiveness and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

A regenerative braking system and hydraulic braking system are used in conjunction in the majority of electric vehicles worldwide. We propose a new regenerative braking distribution strategy that is based on multi-input fuzzy control logic while considering the influences of the battery's state of charge, the brake strength and the motor speed. To verify the braking performance and recovery economy, this strategy was applied to a battery electric vehicle model and compared with two other improved regenerative braking strategies. The performance simulation was performed using standard driving cycles (NEDC, LA92, and JP1015) and a real-world-based urban cycle in China. The tested braking strategies satisfied the general safety requirements of Europe (as specified in ECE-13H), and the emergency braking scenario and economic potential were tested. The simulation results demonstrated the differences in the braking force distribution performance of these three regenerative braking strategies, the feasibility of the braking methods for the proposed driving cycles and the energy economic potential of the three strategies. [ABSTRACT FROM AUTHOR]

System frequency may change from defined values while transmitting power from one area to another in an interconnected power system due to various reasons such as load changes and faults. This frequency change causes a frequency error in the system. However, the system frequency should always be maintained close to the nominal value even in the presence of model uncertainties and physical constraints. This paper proposes an Active Disturbance Rejection Controller (ADRC)-based load frequency control (LFC) of an interconnected power system. The controller incorporates effects of generator inertia and generator electrical proximity to the point of disturbances. The proposed controller reduces the magnitude error of the area control error (ACE) of an interconnected power system compared to the standard controller. The simulation results verify the effectiveness of proposed ADRC in the application of LFC of an interconnected power system. [ABSTRACT FROM AUTHOR]

Photovoltaic-thermal (PVT) technology refers to the integration of a photovoltaic (PV) and a conventional solar thermal collector, representing the deep exploitation and utilization of solar energy. In this paper, we evaluate the performance of a solar PVT cogeneration system based on specific building energy demand using theoretical modeling and experimental study. Through calculation and simulation, the dynamic heating load and electricity load is obtained as the basis of the system design. An analytical expression for the connection of PVT collector array is derived by using basic energy balance equations and thermal models. Based on analytical results, an optimized design method was carried out for the system. In addition, the fuzzy control method of frequency conversion circulating water pumps and pipeline switching by electromagnetic valves is introduced in this paper to maintain the system at an optimal working point. Meanwhile, an experimental setup is established, which includes 36 PVT collectors with every 6 PVT collectors connected in series. The thermal energy generation, thermal efficiency, power generation and photovoltaic efficiency have been given in this paper. The results demonstrate that the demonstration solar PVT cogeneration system can meet the building energy demand in the daytime in the heating season. [ABSTRACT FROM AUTHOR]

A novel polar fuzzy (PF) control approach for a hybrid power system is proposed in this research. The proposed control scheme remedies the issues of system frequency and the continuity of demand supply caused by renewable sources' uncertainties. The hybrid power system consists of a wind turbine generator (WTG), solar photovoltaics (PV), a solar thermal power generator (STPG), a diesel engine generator (DEG), an aqua-electrolyzer (AE), an ultra-capacitor (UC), a fuel-cell (FC), and a flywheel (FW). Furthermore, due to the high cost of the battery energy storage system (BESS), a new idea of vehicle-to-grid (V2G) control is applied to use the battery of the electric vehicle (EV) as equivalent to large-scale energy storage units instead of small batteries to improve the frequency stability of the system. In addition, EV customers' convenience is taken into account. A minimal-order observer is used to estimate the supply error. Then, the area control error (ACE) signal is calculated in terms of the estimated supply error and the frequency deviation. ACE is considered in the frequency domain. Two PF approaches are utilized in the intended system. The mission of each controller is to mitigate one frequency component of ACE. The responsibility for ACE compensation is shared among all parts of the system according to their speed of response. The performance of the proposed control scheme is compared to the conventional fuzzy logic control (FLC). The effectiveness and robustness of the proposed control technique are verified by numerical simulations under various scenarios. [ABSTRACT FROM AUTHOR]

Future power systems control will require large-scale activation of reserves at distribution level. Despite their high potential, distributed energy resources (DER) used for frequency control pose challenges due to unpredictability, grid bottlenecks, etc. To deal with these issues, this study presents a novel strategy of power frequency characteristic dynamic adjustment based on the imbalance state. This way, the concerned operators become aware of the imbalance location but also a more accurate redistribution of responsibilities in terms of reserves activations is achieved. The proposed control is based on the concept of "cells" which are power systems with operating capabilities and responsibilities similar to control areas (CAs), but fostering the use of resources at all voltage levels, particularly distribution grids. Control autonomy of cells allows increased RES hosting. In this study, the power frequency characteristic of a cell is adjusted in real time by means of a fuzzy controller, which curtails part of the reserves, in order to avoid unnecessary deployment throughout a synchronous area, leading to a more localised activation and reducing losses, congestions and reserves exhaustion. Simulation tests in a four-cell reference power system prove that the controller significantly reduces the use of reserves without compromising the overall stability. [ABSTRACT FROM AUTHOR]

A DC microgrid (DC-MG) is a novel power system that uses DC distribution in order to provide high quality power. The study system is made by a photovoltaic array (PV), a wind generator (WG), a fuel cell (FC), and an energy storage system (ESS) to establish a small type DC microgrid, with the bus being established by DC/DC converters with fuzzy controllers. An overall power dispatch was designed for the proposed system to distribute the power flows among the different energy sources and the storage unit in the system in order to satisfy the load requirements throughout an entire 24-h period. The structure of a power supervisor based on an optimal power dispatch algorithm is here proposed. Optimization was performed using dynamic programming (DP). In this paper, a system configuration of a DC microgrid is analyzed in different scenarios to show the efficacy of the control for all devices for the variable weather conditions with different DC loads. Thus, the voltage level and the power flow of the system are shown for different load conditions. [ABSTRACT FROM AUTHOR]

This paper presents the control and simulation of an electric vehicle (EV) charging station using a three-level converter on the grid-side as well as on the EV-side. The charging station control schemes with three-level AC/DC power conversion and a bidirectional DC/DC charging regulator are described. The integration of EVs to the power grid provides an improvement of the grid reliability and stability. EVs are considered an asset to the smart grid to optimize effective performance economically and environmentally under various operation conditions, and more significantly to sustain the resiliency of the grid in the case of emergency conditions and disturbance events. The three-level grid side converter (GSC) can participate in the reactive power support or grid voltage control at the grid interfacing point or the common coupling point (PCC). A fuzzy logic proportional integral (FL-PI) controller is proposed to control the GSC converter. The controllers used are verified and tested by simulation to evaluate their performance using MATLAB/SIMULINK. The comparison of a PI-controller and a PI-Fuzzy controller for the EV charging station shows the effectiveness of the proposed FL-PI controller over conventional PI controller for same circuit operating conditions. A good performance for PI-Fuzzy in terms of settling time and peak overshoot can observed from the simulation results. [ABSTRACT FROM AUTHOR]

This paper describes the complete integration of a fuzzy control of multiple evaporator systems with the IEEE 802.15.4 standard, in which we study several important aspects for this kind of system, like a detailed analysis of the end-to-end real-time flows over wireless sensor and actuator networks (WSAN), a real-time kernel with an earliest deadline first (EDF) scheduler, periodic and aperiodic tasking models for the nodes, lightweight and flexible compensation-based control algorithms for WSAN that exhibit packet dropouts, an event-triggered sampling scheme and design methodologies. We address the control problem of the multi-evaporators with the presence of uncertainties, which was tackled through a wireless fuzzy control approach, showing the advantages of this concept where it can easily perform the optimization for a set of multiple evaporators controlled by the same smart controller, which should have an intelligent and flexible architecture based on multi-agent systems (MAS) that allows one to add or remove new evaporators online, without the need for reconfiguring, while maintaining temporal and functional restrictions in the system. We show clearly how we can get a greater scalability, the self-configuration of the network and the least overhead with a non-beacon or unslotted mode of the IEEE 802.15.4 protocol, as well as wireless communications and distributed architectures, which could be extremely helpful in the development process of networked control systems in large spatially-distributed plants, which involve many sensors and actuators. For this purpose, a fuzzy scheme is used to control a set of parallel evaporator air-conditioning systems, with temperature and relative humidity control as a multi-input and multi-output closed loop system; in addition, a general architecture is presented, which implements multiple control loops closed over a communication network, integrating the analysis and validation method for multi-loop control networks designed for multi-evaporator systems. [ABSTRACT FROM AUTHOR]

This paper presents a voltage control method using multiple distributed generators (DGs) based on a multi-agent system framework. The output controller of each DG is represented as a DG agent, and each voltage-monitoring device is represented as a monitoring agent. These agents cooperate to accomplish voltage regulation through a coordinating agent or moderator. The moderator uses the reactive power sensitivities and margins to determine the voltage control contributions of each DG. A fuzzy inference system (FIS) is employed by the moderator to manage the decision-making process. An FIS scheme is developed and optimized to enhance the efficiency of the proposed voltage control process using particle swarm optimization. A simple distribution system with four voltage-controllable DGs is modeled, and an FIS moderator is implemented to control the system. Simulated data show that the proposed voltage control process is able to maintain the system within the operating voltage limits. Furthermore, the results were similar to those obtained using optimal power flow calculations, even though little information on the power system was required and no power flow calculations were implemented. [ABSTRACT FROM AUTHOR]

In order to realize the online learning of a hybrid electric vehicle (HEV) control strategy, a fuzzy Q-learning (FQL) method is proposed in this paper. FQL control strategies consists of two parts: The optimal action-value function Q*(x,u) estimator network (QEN) and the fuzzy parameters tuning (FPT). A back propagation (BP) neural network is applied to estimate Q*(x,u) as QEN. For the fuzzy controller, we choose a Sugeno-type fuzzy inference system (FIS) and the parameters of the FIS are tuned online based on Q*(x,u). The action exploration modifier (AEM) is introduced to guarantee all actions are tried. The main advantage of a FQL control strategy is that it does not rely on prior information related to future driving conditions and can self-tune the parameters of the fuzzy controller online. The FQL control strategy has been applied to a HEV and simulation tests have been done. Simulation results indicate that the parameters of the fuzzy controller are tuned online and that a FQL control strategy achieves good performance in fuel economy. [ABSTRACT FROM AUTHOR]

The in-wheel electric vehicle is expected to be a popular next-generation vehicle because an in-wheel system can simplify the powertrain and improve driving performance. In addition, it also has an advantage in that it maximizes driving efficiency through independent torque control considering the motor efficiency. However, there is an instability problem if only the driving torque is controlled in consideration of only the motor efficiency. In this paper, integrated torque distribution strategies are proposed to overcome these problems. The control algorithm consists of various strategies for optimizing driving efficiency, satisfying driver demands, and considering tire slip and vehicle cornering. Fuzzy logic is used to determine the appropriate timing of intervention for each distribution strategy. A performance simulator for in-wheel electric vehicles was developed by using MATLAB/Simulink and CarSim to validate the control strategies. From simulation results under complex driving conditions, the proposed algorithm was verified to improve both the driving stability and fuel economy of the in-wheel vehicle. [ABSTRACT FROM AUTHOR]

This paper presents the design of a fuzzy-logic-based voltage-regulated solar power maximum power point tracking (MPPT) system for applications involving hybrid power systems. The system contains a solar power system and battery as the primary and secondary power sources, respectively. The solar system alone supplies power to the electric motor and maintains the output voltage at a predetermined level when it has sufficient power. When the solar power is insufficient, the solar system is operated at its maximum power point (MPP) and the battery is engaged to compensate for the insufficiency. First, a variant of the incremental conductance MPP condition was established. Under the MPP condition, the voltage-regulated MPPT system was formulated as a feedback control system, where the MPP condition and voltage regulation requirements were used as the system inputs. Next, a fuzzy controller was developed to perform the voltage-regulated MPPT function for the hybrid power system. A simulation model based on Matrix laboratory (MATLAB)/SIMULINK (a block diagram environment for multi-domain simulation and model-based design) and a piecewise linear electric circuit simulation (PLECS) tool for controlling the dc motor velocity was developed to verify the voltage-regulated solar power MPPT system. [ABSTRACT FROM AUTHOR]

This study applied a model predictive control (MPC) framework to solve the cruising control problem of a series hydraulic hybrid vehicle (SHHV). The controller not only regulates vehicle velocity, but also engine torque, engine speed, and accumulator pressure to their corresponding reference values. At each time step, a quadratic programming problem is solved within a predictive horizon to obtain the optimal control inputs. The objective is to minimize the output error. This approach ensures that the components operate at high efficiency thereby improving the total efficiency of the system. The proposed SHHV control system was evaluated under urban and highway driving conditions. By handling constraints and input-output interactions, the MPC-based control system ensures that the system operates safely and efficiently. The fuel economy of the proposed control scheme shows a noticeable improvement in comparison with the PID-based system, in which three Proportional-Integral-Derivative (PID) controllers are used for cruising control. [ABSTRACT FROM AUTHOR]

The power converter is one of the essential elements for effective use of renewable power sources. This paper focuses on the development of a circuit simulation model for maximum power point tracking (MPPT) evaluation of solar power that involves using different buck-boost power converter topologies; including SEPIC, Zeta, and four-switch type buck-boost DC/DC converters. The circuit simulation model mainly includes three subsystems: a PV model; a buck-boost converter-based MPPT system; and a fuzzy logic MPPT controller. Dynamic analyses of the current-fed buck-boost converter systems are conducted and results are presented in the paper. The maximum power point tracking function is achieved through appropriate control of the power switches of the power converter. A fuzzy logic controller is developed to perform the MPPT function for obtaining maximum power from the PV panel. The MATLAB-based Simulink piecewise linear electric circuit simulation tool is used to verify the complete circuit simulation model. [ABSTRACT FROM AUTHOR]

In this paper, an original scheduling approach for optimal dispatch of electrical Energy Storage Systems (ESS) in modern distribution networks is proposed. The control system is based on fuzzy rules and does not use forecasts since it repairs the past history according to the real time data on the electrical energy cost, renewable energy production and load. When the system detects a worsening of performances, the fuzzy logic rule-based control system self-adapts its membership functions using an economic indicator. The common use, in the relevant literature, of forecasted values in such systems can lead to large errors and economic losses. Moreover the speed of calculation guaranteed by the fuzzy control system allows the execution of new calculations even with high frequency. After the Introduction section, where the state of the art on the topic is outlined, the problem formulation is presented and an interesting application of the considered approach to the control on a medium size battery with real world data is proposed. [ABSTRACT FROM AUTHOR]

A digital H∞ controller for a permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS) is presented. Wind energy is an uncertain fluctuating resource which requires a tight control management. So, it is still an exigent task for the control design engineers. The conventional proportional-integral (PI) control is not ideal during high turbulence wind velocities, and the nonlinear behavior of the power converters. These are raising interest towards the robust control concepts. The robust design is to find a controller, for a given system, such that the closed-loop system becomes robust that assurance high-integrity and fault tolerant control system, robust H∞ control theory has befallen a standard design method of choice over the past two decades in industrial control applications. The robust H∞ control theory is also gaining eminence in the WECS. Due to the implementation complexity for the continuous H∞ controller, and availability of the high speedy micro-controllers, the design of a sample-data or a digital H∞ controller is very important for the realistic implementation. But there isn't a single research to evaluate the performance of the digital H∞ controller for the WECS. In this paper, the proposed digital H∞ controller schemes comprise for the both generator and grid interactive power converters, and the control performances are compared with the conventional PI controller and the fuzzy controller. Simulation results confirm the efficacy of the proposed method which are ensured the WECS stabilities, mitigate shaft stress, and improving the DC-link voltage and output power qualities [ABSTRACT FROM AUTHOR]

Variable speed wind turbines are commonly used as wind power generation systems because of their lower maintenance cost and flexible speed control. The optimum output power for a wind turbine can be extracted using maximum power point tracking (MPPT) strategies. However, unpredictable parameters, such as wind speed and air density could affect the accuracy of the MPPT methods, especially during the wind speed small oscillations. In this paper, in a permanent magnet synchronous generator (PMSG), the MPPT is implemented by determining the uncertainty of the unpredictable parameters using the extended Kalman filter (EKF). Also, the generator speed is controlled by employing a fuzzy logic control (FLC) system. This study aims at minimizing the effects of unpredictable parameters on the MPPT of the PMSG system. The simulation results represent an improvement in MPPT accuracy and output power efficiency. [ABSTRACT FROM AUTHOR]

Natural ventilation is gaining more attention from architects and engineers as an alternative way of cooling and ventilating indoor spaces. Based on building types, it could save between 13 and 40% of the building cooling energy use. However, this needs to be implemented and operated with a well-designed and integrated control system to avoid triggering discomfort for occupants. This paper seeks to review, discuss, and contribute to existing knowledge on the application of control systems and optimisation theories of naturally ventilated buildings to produce the best performance. The study finally presents an outstanding theoretical context and practical implementation for researchers seeking to explore the use of intelligent controls for optimal output in the pursuit to help solve intricate control problems in the building industry and suggests advanced control systems such as fuzzy logic control as an effective control strategy for an integrated control of ventilation, heating and cooling systems. [ABSTRACT FROM AUTHOR]

Alternating current electric drives are widely used in many commercial applications. Increasing energy efficiency and improving the quality of regulation are major goals. The speed control structure of the induction machine with indirect vector control with rotor flux orientation and the speed control structure of the permanent magnet synchronous machine with vector control with rotor flux orientation are taken into account. The control structures were modeled and simulated in Simulink. Different simulation regimes were made for different load torques and different parameters. Following the analyses performed, it was highlighted that the fuzzy regulation ensures the reduction of the energy consumption in transient states and the improvement in the quality indicators of the regulation. The fuzzy control systems are robust to the errors of identification of the machine parameters and to the effect of the disturbing load torque. [ABSTRACT FROM AUTHOR]

Because of the unpredictable activity of solar energy sources, photovoltaic (PV) maximum power point tracking (MPPT) is essential to guarantee the continuous operation of electrical energy generation at optimal power levels. Several works have extensively examined the generation of the maximum power from the PV systems under normal and shading conditions. The fuzzy logic control (FLC) method is one of the effective MPPT techniques, but it needs to be adapted to work in partial shading conditions. The current paper presents the FLC-based on dynamic safety margin (DSM) as an MPPT technique for a PV system to overcome the limitations of FLC in shading conditions. The DSM is a performance index that measures the system state deviation from the normal situation. As a performance index, DSM is used to adapt the FLC controller output to rapidly reach the global maxima of the PV system. The ability of the proposed algorithm and its performance are evaluated using simulation and practical implementation results for single phase grid-connected PV system under normal and partial shading operating conditions. [ABSTRACT FROM AUTHOR]

Modern energy automation solutions and demand response applications rely on load profiles to monitor and manage electricity consumption effectively. The introduction of smart control systems capable of handling additional fuzzy parameters, such as weather data, through machine learning methods, offers valuable insights in an attempt to adjust consumer behavior optimally. Following recent advances in the field of fuzzy control, this study presents the design and implementation of a fuzzy control system that processes environmental data in order to recommend minimum energy consumption values for a residential building. This system follows the forward chaining Mamdani approach and uses decision tree linearization for rule generation. Additionally, a hybrid feature selector is implemented based on XGBoost and decision tree metrics for feature importance. The proposed structure discovers and generates a small set of fuzzy rules that highlights the energy consumption behavior of the building based on time-series data of past operation. The response of the fuzzy system based on sample input data is presented, and the evaluation of its performance shows that the rule base generation is derived with improved accuracy. In addition, an overall smaller set of rules is generated, and the computation is faster compared to the baseline decision tree configuration. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have become widespread during the last decade because of the distinct advantages they offer compared to the conventional ones. However, the increased penetration of EVs in the global transportation market has led increased electricity demands, which is expected to affect the operation of energy distribution systems. In the present paper, a demonstration about the effects of uncontrolled EVs charging in a case study low voltage (LV) network is demonstrated and a fuzzy energy management strategy for the coordination of EV charging in LV networks is presented, by including the distance of the EVs from the transformers in the fuzzy management systems for the first time. The Institute of Electrical and Electronics Engineers (IEEE) European Test Feeder is used as a case study low voltage distribution grid. In particular, the developed system configuration takes into consideration the architecture of the grid, the ampacities of the lines and the voltages at the system's buses. Moreover, electric vehicles are considered as agent-based models, which are characterized by the model of each EV, the state-of-charge of their batteries and the charging power. In particular, an investigation into the effects of uncontrolled charging is performed, in which two approaches are examined. The first approach investigates the maximum number of chargeable EVs in the case study network and how it is influenced by the grid's household loads. The second approach examines the number of network undervoltages and lines ampacity violations in a set of simulation scenarios. The results of the first approach show that the distance of the EVs from the networks substation affects the maximum number of chargeable EVs in a significant manner. Based on the observed results of the two approaches, a fuzzy management system is designed for the coordination of EV changing, which takes into account the distance from the EV charging points to the feeder substation, the state-of-charge of the EVs' batteries and the EVs' charging delay time. [ABSTRACT FROM AUTHOR]

The paper presents the complete design processes of novel aeration control systems in the SBR (sequencing batch reactor) wastewater treatment plant (WWTP). Due to large energy expense and a high influence on biological processes, the aeration system plays a key role in WWTP operation. The paper considers the aeration system for a biological WWTP located in the northeast of Poland. This system consists of blowers, the main collector pipeline, three aeration lines with different diameters and lengths, and diffusers. Classical control systems applied for this type of installation are based on PID (proportional–integral–derivative) controllers, the settings of which are often found experimentally. The article presents the optimization of these settings and the design of an alternative control algorithm—the fuzzy controller. [ABSTRACT FROM AUTHOR]

The energy consumption of air-conditioning systems is a major part of energy consumption in buildings. Optimal control strategies have been increasingly developed in building heating, ventilation, and air-conditioning (HVAC) systems. In this paper, a load forecast fuzzy (LFF) control strategy was proposed. The predictive load based on the SVM method was used as the input parameter of the fuzzy controller to perform feedforward fuzzy control on the HVAC system. This control method was considered as an effective way to reduce energy consumption while ensuring indoor comfort, which can solve the problem of hysteresis and inaccuracy in building HVAC systems by controlling the HVAC system in advance. The case study was conducted on a ground source heat pump system in Tianjin University to validate the proposed control strategy. In addition, the advantages of the LFF control strategy were verified by comparing with two feedback control strategies, which are the supply water temperature (SWT) control strategy and the room temperature fuzzy (RTF) control strategy. Results show that the proposed LFF control strategy is capable not only to ensure the minimum indoor temperature fluctuations but also decrease the total energy consumption. [ABSTRACT FROM AUTHOR]

Recently, wind energy conversion systems in renewable energy sources have attracted attention due to their effective application. Wind turbine systems have a complex structure; however, traditional control systems are inadequate in answering the demands of complex systems. Therefore, expert control systems are applied to wind turbines, such as type-1 and interval type-2 fuzzy logic control (IT-2 FLC) systems. An IT-2 FLC system is used to solve the complexity of the wind turbine system and increases the efficiency of the wind turbine. This paper proposes a new control approach using the IT-2 FLC method applied to a wind turbine based on a permanent magnet synchronous generator (PMSG) to improve the transient stability during grid faults. An IT-2 FLC was designed to enhance the fault ride-through performance of a wind turbine and was implemented to control the machine side converter and grid side converter of a wind turbine. The proposed algorithm performance of a wind turbine based on a PMSG was investigated for different types of grid fault. The analysis results verify that the interval type-2 fuzzy logic control system is robustly utilized under different operational conditions. [ABSTRACT FROM AUTHOR]

This paper deals with a methodical design approach of fault-tolerant controller that gives assurance for the the stabilization and acceptable control performance of the nonlinear systems which can be described by Takagi–Sugeno (T–S) fuzzy models. Takagi–Sugeno fuzzy model gives a unique edge that allows us to apply the traditional linear system theory for the investigation and blend of nonlinear systems by linear models in a different state space region. The overall fuzzy model of the nonlinear system is obtained by fuzzy combination of the all linear models. After that, based on this linear model, we employ parallel distributed compensation for designing linear controllers for each linear model. Also this paper reports of the T–S fuzzy system with less conservative stabilization condition which gives decent performance. However, the controller synthesis for nonlinear systems described by the T–S fuzzy model is a complicated task, which can be reduced to convex problems linking with linear matrix inequalities (LMIs). Further sufficient conservative stabilization conditions are represented by a set of LMIs for the Takagi–Sugeno fuzzy control systems, which can be solved by using MATLAB software. Two-rule T–S fuzzy model is used to describe the nonlinear system and this system demonstrated with proposed fault-tolerant control scheme. The proposed fault-tolerant controller implemented and validated on three interconnected conical tank system with two constraints in terms of faults, one issed to build the actuator and sond is system component (leak) respectively. The MATLAB Simulink platform with linear fuzzy models and an LMI Toolbox was used to solve the LMIs and determine the controller gains subject to the proposed design approach. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy logic-based controller for a wind turbine system to provide frequency support for a smart grid. The designed controller is aimed to provide an appropriate dynamic droop rate depending on the local measurements of each wind turbine of a wind farm such as the maximum power available and the amount of power reserve. The designed fuzzy controller depends on the rate of change of frequency (ROCOF) at the point of common coupling (PCC). The main advantage of the proposed fuzzy controller is to provide frequency support by the wind turbine system connected to a smart grid. The dynamic rate of the controller is defined by the fuzzy sets considering the change in the grid's frequency and the available reserve power. First, the response of static droop curves is investigated for different scenarios of wind turbines connected to a smart grid. Then, the proposed fuzzy logic-based droop controller is integrated into the system, and its performance and response are evaluated, and the results are compared with static-droop based controller. The proposed controller is tested using Matlab\Simulink. [ABSTRACT FROM AUTHOR]

In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption. [ABSTRACT FROM AUTHOR]

The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system. [ABSTRACT FROM AUTHOR]

One of the most crucial and economically-beneficial tasks for energy customers is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may vary. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data-driven forecasting method based on fuzzy logic (FL) for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, namely left and right from load peak, for the sake of searching for equal BESS capacity. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) considering BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak power shaving/curtailment method, offline digital time-domain simulations have been performed on a test microgrid system using the data gathered from a real-life practical test microgrid system in the MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical microgrid system with real-life data obtained from smart meters and also, compared with several previously-reported methods. [ABSTRACT FROM AUTHOR]

With the growing of using photovoltaic (PV) units in power distribution systems, the role of high-performance power electronic converters is increasing. In this paper, modelling and control of Modular Multilevel Converter (MMC) are addressed for grid integration of PV units. Designing a proper controller for MMC is crucial during faulty conditions to make the converter stable and provide proper dynamic performance. To achieve this goal, a dynamic model of MMC is presented which it includes symmetrical components of voltage and current. Then, adaptive robust current controllers are developed based on sliding mode and fuzzy controllers for MMC and then the robustness and stability of the controllers are proved by the Lyapunov theory. To implement the proposed controllers under unbalanced grid voltage fault, positive and negative sequences current controllers are implemented to compensate the effect of grid voltage fault and load power variation. Finally, numerical results are shown to evaluate the performance of MMC. In the end, the experimental results are given to prove the controller performance. The outcome indicates that the proposed current controllers are more effective under voltage disturbance conditions and could satisfy the stability of MMC. [ABSTRACT FROM AUTHOR]

In order to enhance the steering stability of a four hub-motor independent-drive electric vehicle (4MIDEV) on a road with varying adhesion coefficient, for example on a joint road, this paper proposes a hierarchical steering stability control strategy adapted to the road adhesion. The upper control level of the proposed strategy realizes the integrated control of the sideslip angle and yaw rate in the direct yaw moment control (DYC), where the influences of the road adhesion and sideslip angle are both studied by the fuzzy control. The lower control level employs a weight-based optimal torque distribution algorithm in which weight factors for each motor torque are designed to accommodate different adhesion of each wheel. The proposed stability control strategy was validated in a co-simulation of the Carsim and Matlab/Simulink platforms. The results of double-lane-change maneuver simulations under different conditions indicate that the proposed strategy can effectively achieve robustness to changes in the adhesion coefficient and improve the steering stability of the 4MIDEV. [ABSTRACT FROM AUTHOR]

The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants’ production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model. [ABSTRACT FROM AUTHOR]

The stator side of a double-fed induction generator (DFIG) is directly connected to the grid, so the DFIG is sensitive to a voltage drop caused by power system faults. A double resistors braking method based on fuzzy control is proposed to improve the performance of low-voltage ride through (LVRT) in this paper. Based on the mathematical model of DFIG, it analyzes the function of a series dynamical braking resistor (SDBR) theoretically. The series impedance value of the SDBR is determined by the variation of the rotor's open circuit voltage, the voltage and current of the stator and the rotor, and also the heat capacity of the SDBR. In order to improve the LVRT capability of a DFIG under different fault grads, a double series resistors braking mode is presented. Through adopting a fuzzy control strategy, double series resistor switching is implemented. With the example system, the correctness and validity of the proposed method is verified. [ABSTRACT FROM AUTHOR]