An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

Renewable energy sources experience problems such as deregulation when they are used as stand-alone energy sources. This paper presents an optimal power sharing and power control strategy combining a photovoltaic (PV) array, a fuel cell (FC) stack, an ultra-capacitor (UC) module, and a set of loads. The photovoltaic is the prior energy source while the fuel cell (FC) system is added as a backup source to meet the excess power demand. The ultra-capacitor (UC) is utilized as a buffer storage to compensate the slow dynamic response of the FC during transient and regulate the DC-bus voltage. The power control strategy is designed to work on a two-level arrangement. The top level controls the entire power management, which generates references to low level individual subsystems depending upon solar radiation, temperature, and load conditions. Based on the command signals, each local controller controls the PV, FC, electrolyzer, and UC. The top level also controls the load scheduling during low solar radiation in order to sustain the system operation for 24 h. The performance of the system is tested under real-world record of solar radiation, temperature, and load conditions for Bahria town at Islamabad, Pakistan. The effectiveness of the proposed model in terms of voltage regulation, power transfer, load tracking, and grid stability is verified by Matlab simulation results.

Abstract:The charging infrastructure plays a key role in the healthy and rapid development of the electric vehicle industry. This paper presents an energy management and control system of an electric vehicle charging station. The charging station (CS) is integrated to a grid-connected hybrid power system having a wind turbine maximum power point tracking (MPPT) controlled subsystem, photovoltaic (PV) MPPT controlled subsystem and a controlled solid oxide fuel cell with electrolyzer subsystem which are characterized as renewable energy sources. In this article, an energy management system is designed for charging and discharging of five different plug-in hybrid electric vehicles (PHEVs) simultaneously to fulfil the grid-to-vehicle (G2V), vehicle-to-grid (V2G), grid-to-battery storage system (G2BSS), battery storage system-to-grid (BSS2G), battery storage system-to-vehicle (BSS2V), vehicle-to-battery storage system (V2BSS) and vehicle-to-vehicle (V2V) charging and discharging requirements of the charging station. A simulation test-bed in Matlab/Simulink is developed to evaluate and control adaptively the AC-DC-AC converter of non-renewable energy source, DC-DC converters of the storage system, DC-AC grid side inverter and the converters of the CS using adaptive proportional-integral-derivate (AdapPID) control paradigm. The effectiveness of the AdapPID control strategy is validated through simulation results by comparing with conventional PID control scheme.

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In the current smart grid scenario, the evolution of a proficient and robust maximum power point tracking (MPPT) algorithm for a PV subsystem has become imperative due to the fluctuating meteorological conditions. In this paper, an adaptive feedback linearization-based NeuroFuzzy MPPT (AFBLNF-MPPT) algorithm for a photovoltaic (PV) subsystem in a grid-integrated hybrid renewable energy system (HRES) is proposed. The performance of the stated (AFBLNF-MPPT) control strategy is approved through a comprehensive grid-tied HRES test-bed established in MATLAB/Simulink. It outperforms the incremental conductance (IC) based adaptive indirect NeuroFuzzy (IC-AIndir-NF) control scheme, IC-based adaptive direct NeuroFuzzy (IC-ADir-NF) control system, IC-based adaptive proportional-integral-derivative (IC-AdapPID) control scheme, and conventional IC algorithm for a PV subsystem in both transient as well as steady-state modes for varying temperature and irradiance profiles. The comparative analyses were carried out on the basis of performance indexes and efficiency of MPPT.

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Precisely measuring the work area of agriculture farm machinery is important for performing the authentication of machinery usage, better allocation of resources, measuring the effect of machinery usage on the yield, usage billing and driver’s behaviour. The manual measurement, which is a common practice is an error-prone and time-consuming process. The irregular fields make it even more difficult to calculate the work area. An automatic solution that uses smart technology and algorithms to precisely calculate the work area is crucial for the advancement of agriculture. In this work, we have developed a smart system that utilizes the Internet of Things (IoT), Global Positioning System (GPS) and Artificial Intelligence (AI) that records the movement of agriculture machinery and use it to measure the precise work area of its usage. The system couples the nearest neighbourhood algorithms with Contact-based mechanisms to find the precise work area for different shaped fields and activities. The system was able to record the movement of machinery and calculate its work area, regardless of how many times the machinery runs through a particular field. Our evaluation shows that the system was able to precisely find the work boundaries and calculate the area with a maximum of 9% error for irregular shapes.

Efficient extraction and conversion of solar power is an important research area in the field of renewable energy integration. Although, literature is enriched with different power extraction, conversion, and harmonics elimination techniques but still there are problems of maximum power point tracking (MPPT) under partial shading conditions, photovoltaic (PV) mismatching, power fluctuation in steady‐state condition, and higher total harmonic distortion (THD) value. The article has two manifolds. First, it proposes a fuzzy embedded MPPT controller to extract the maximum available PV power under time‐varying environmental conditions. For that, it utilizes seven linguistic variables and fuzzy sets along with 49 rules base to have reduced power fluctuation and power losses in a steady‐state condition, and fast tracking under time‐varying temperature and solar irradiance condition. The performance of the proposed fuzzy‐logic control (FLC)‐49 rules base is compared with FLC‐25 rules and perturb & observe based MPPT controllers. Second, for fulfilling the load demands with lower switching losses and THD, particle swarm optimization (PSO) based selective harmonic elimination (SHE) technique is applied on a cascaded half‐bridge multilevel inverter. SHE technique utilizes switching angles, which are found by solving the nonlinear transcendental equations obtained by Fourier series expansion of stepped output voltage waveform. For that, the proposed research preferred meta‐heuristic techniques of PSO over the classical techniques because of their high convergence rate, short run time, less complexity, and ability to obtain lower THD.