A Novel Intelligent Neural Network Techniques of UPQC with Integrated Solar PV System for Power Quality Enhancement

A Novel, Intelligent control of a Unified Power Quality Conditioner (UPQC), coupled with a Photovoltaic (PV) system, is proposed in this work. It enhances the decarbonizes clean energy generation and maintains Power Quality (PQ) to the grid. In PV integrated UPQC, Crow Search Algorithm (CSA) assisted Perturb and Observation (P&O) Maximum Power Point Tracking (MPPT) technique. A d-q theory-based control is employed with the assistance of a Proportional Integral (PI) controller for controlling the working of UPQC and maintaining the power quality. The dynamic working of the PV-based UPQC is evaluated based on simulation outcomes attained from MATLAB

A Novel Intelligent Neural Network Techniques of UPQC with Integrated Solar PV System for Power Quality Enhancement

A Novel, Intelligent control of a Unified Power Quality Conditioner (UPQC), coupled with a Photovoltaic (PV) system, is proposed in this work. It enhances the decarbonizes clean energy generation and maintains Power Quality (PQ) to the grid. In PV integrated UPQC, Crow Search Algorithm (CSA) assisted Perturb and Observation (P&O) Maximum Power Point Tracking (MPPT) technique. A d-q theory-based control is employed with the assistance of a Proportional Integral (PI) controller for controlling the working of UPQC and maintaining the power quality. The dynamic working of the PV-based UPQC is evaluated based on simulation outcomes attained from MATLAB

Current control strategy of grid-connected inverter for distributed generation under nonliner load conditions

Distributed generation (DG) has become more important in recent years for supplementing traditional fossil energy resources for power generation. The DGs include microturbine (MT), fuel cell, photovoltaic (PV) arrays, wind turbine and storage devices. The DG units can operate in parallel to the main grid or in a microgrid (MG) mode. The MG is a discrete energy system consisting of DG and loads that are capable of operating in parallel with, or independently from the main grid. Meanwhile, Grid-Connected Inverters (GCIs) are typically used as the interfaces to connect each DG to the common bus in an MG mode. In the ongoing effort to improve the performance of MG, control strategy of three-phase GCI under nonlinear load conditions has become a mature and well-developed research topic, and some control strategies have been implemented in several countries. A new approach is proposed to control the GCI of DG in an MG under nonlinear and unbalanced load conditions. The proposed control strategy features the synchronous reference frame method. The primary advantage of this method is its ability to effectively compensate for the harmonic current content of the system currents and MG without using any compensation devices, such as an Active Power Filter (APF) or passive filter. In this system, the control strategy is designed to eliminate the main harmonics as well as to cancel the remaining harmonics. Furthermore, correction of the system unbalance is another key feature of the proposed strategy. Fast dynamic response, simple design, stability, and fast transient response are other key features of the presented strategy. The current total harmonic distortions were reduced from above 37.8% to less than 1% with the proposed control strategy under nonlinear load conditions. The proposed control method can be used on the GCI of MT and PV; and has the ability to reduce the complexity, size and cost of the control method in comparison with APFs

Premium Power Value-Added Service Product Decision-Making Method Based on Multi-Index Two-Sided Matching

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The JM-Filter to detect specific frequency in monitored signal

The Discrete Fourier Transform (DFT) is a mathematical procedure that stands at the center of the processing inside a digital signal processor. It has been widely known and argued in relevant literature that the Fast Fourier Transform (FFT) is useless in detecting specific frequencies in a monitored signal of length N because most of the computed results are ignored. In this paper, we present an efficient FFT-based method to detect specific frequencies in a monitored signal, which will then be compared to the most frequently used method which is the recursive Goertzel algorithm that detects and analyses one selectable frequency component from a discrete signal. The proposed JM-Filter algorithm presents a reduction of iterations compared to the first and second order Goertzel algorithm by a factor of r, where r represents the radix of the JM-Filter. The obtained results are significant in terms of computational reduction and accuracy in fixed-point implementation. Gains of 15 dB and 19 dB in signal to quantization noise ratio (SQNR) were respectively observed for the proposed first and second order radix-8 JM-Filter in comparison to Goertzel algorithm

Technical solutions for low-voltage microgrid concept

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Enhancing the performance of flexible AC transmission systems (FACTS) by computational intelligence

The thesis studies and analyzes UPFC technology concerns the management of active and reactive power in the power networks to improve the performance aiming to reach the best operation criteria. The contributions of the thesis start with formatting, deriving, coding and programming the network equations required to link UPFC steady-state and dynamic models to the power systems. The thesis derives GA applications on UPFC to achieve real criteria on a real world sub-transmission network. An enhanced GA technique is proposed by enhancing and updating the working phases of the GA including the objective function formulation and computing the fitness using the diversity in the population and selection probability. The simulations and results show the advantages of using the proposed technique. Integrating the results by linking the case studies of the steady-state and the dynamic analysis is achieved. In the dynamic analysis section, a new idea for integrating the GA with ANFIS to be applied on the control action procedure is presented. The main subject of the thesis deals with enhancing the steady-state and dynamics performance of the power grids by Flexible AC Transmission System (FACTS) based on computational intelligence. Control of the electric power system can be achieved by designing the FACTS controller, where the new trends as Artificial Intelligence can be applied to this subject to enhance the characteristics of controller performance. The proposed technique will be applied to solve real problems in a Finnish power grid. The thesis seeks to deal, solve, and enhance performances until the year 2020, where the data used is until the conditions of year 2020. The FACTS device, which will be used in the thesis, is the most promising one, which known as the Unified Power Flow Controller (UPFC). The thesis achieves the optimization of the type, the location and the size of the power and control elements for UPFC to optimize the system performance. The thesis derives the criteria to install the UPFC in an optimal location with optimal parameters and then designs an AI based damping controller for enhancing power system dynamic performance. In this thesis, for every operating point GA is used to search for controllers' parameters, parameters found at certain operating point are different from those found at others. ANFISs are required in this case to recognize the appropriate parameters for each operating point

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique