A hybrid renewable energy production system using a smart controller based on fuzzy logic

Introduction. Hybrid electric vehicles are offering the most economically viable choices in today's automotive industry, providing best solutions for a very high fuel economy and low rate of emissions. The rapid progress and development of this industry has prompted progress of human beings from primitive level to a very high industrial society where mobility used to be a fundamental need. However, the use of large number of automobiles is causing serious damage to our environment and human life. At present most of the vehicles are relying on burning of hydrocarbons in order to achieve power of propulsion to drive wheels. Therefore, there is a need to employ clean and efficient vehicles like hybrid electric vehicles. Unfortunately, earlier control strategies of series hybrid electric vehicle fail to include load disturbances during the vehicle operation and some of the variations of the nonlinear parameters (e.g. stator’s leakage inductance, resistance of winding etc.). The novelty of the proposed work is based on designing and implementing two robust sliding mode controllers (SMCs) on series hybrid electric vehicle to improve efficiency in terms of both speed and torque respectively. The basic idea is to let the engine operate only when necessary keeping in view the state of charge of battery. Purpose. In proposed scheme, both performance of engine and generator is being controlled, one sliding mode controllers is controlling engine speed and the other one is controlling generator torque, and results are then compared using 1-SMC and 2-SMC’s. Method. The series hybrid electric vehicle powertrain considered in this work consists of a battery bank and an engine-generator set which is referred to as the auxiliary power unit, traction motor, and power electronic circuits to drive the generator and traction motor. The general strategy is based on the operation of the engine in its optimal efficiency region by considering the battery state of charge. Results .Mathematical models of engine and generator were taken into consideration in order to design sliding mode controllers both for engine speed and generator torque control. Vehicle was being tested on standard cycle. Results proved that, instead of using only one controller for engine speed, much better results are achieved by simultaneously using two sliding mode controllers, one controlling engine speed and other controlling generator torque.

Section: Introduction Advent Of High Pace Development Of Internal Com...mentioning

confidence: 99%

Efficiency enhancement strategy implementation in hybrid electric vehicles using sliding mode control

Ibrar

Ahmad

Safdar

et al. 2023

“…As a solution, a proposed approach involves a simplified structure for the Fuzzy Logic Controller (FLC). Previous studies on line-side converters in high-speed railways have mainly focused on various aspects of controller design, including dynamic performance, stability analysis, and the mitigation of high-order harmonics [20][21][22][23][24].…”

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confidence: 99%

Harmonics suppression in high-speed railway via single-phase traction converter with an LCL filter using fuzzy logic control strategy

Aissaoui,

Bouzeria,

Benidir

et al. 2024

Introduction. The railway Traction Power Supply System (TPSS) encounters a common challenge related to high-frequency harmonic resonance, especially when employing AC-DC-AC traction drive systems in high-speed trains. This resonance issue arises when the harmonic elements introduced by the traction AC-DC converter on the grid side of trains align with the innate resonance frequency of the TPSS. The novelty the proposed work focuses on the challenges associated with resonance elevation and high-frequency harmonics in high-speed trains, while simultaneously enhancing energy quality. This is achieved by integrating a pulse-width-modulated converter on the grid side with a single-phase configuration and incorporating an LCL filter. Methodology. In order to optimize the system’s efficiency, a robust control system is employed, taking advantage of the capabilities of a fuzzy logic controller (FLC). The choice of the FLC is justified by its straightforward design and reliability, emphasizing the dedication to precise control, as fuzzy logic excels in handling complex, nonlinear systems. Through the use of linguistic variables and heuristic reasoning, the FLC adjusts to dynamic changes in the system, demonstrating its efficacy in enhancing both transient and steady-state responses. Practical value. A grid-side LCL filter-based converter was meticulously designed and rigorously simulated using the MATLAB/Simulink platform. The inclusion of an advanced FLC in the system introduced a novel approach to control strategies, surpassing the traditional PI controller. Through a comprehensive comparative analysis, the simulation results showcased the remarkable efficacy of the proposed solution in an effectively mitigating high-frequency resonance within the TPSS. This outcome underscores the potential of FLC as a sophisticated control mechanism for enhancing the performance systems in railway applications, showcasing its superiority over conventional control methods. The study contributes in shedding light on innovative approaches for optimizing the control and efficiency of grid-side LCL filter-based converters in high-speed train systems.

“…Since, both are non-polluting, abundantly available and generate power closer to load centers [1]. The widespread integration of renewable energy sources (RESs) into the utility AC grid causes voltage fluctuations and protection issues [2]. As a result, the utility grid's reliability, security and quality are highly affected.…”

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confidence: 99%

Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications

Sathish

Chidambaram

Manikandan³

2023

Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery’s state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %.