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This study investigated the problem of controlling a three-phase three-wire photovoltaic (PV)-type distribution static compensator (DSTATCOM). In order to model, simulate, and control the system, the MATLAB/SIMULINK tool was used. Different controllers were applied to create switching pulses for the IGBT-based voltage source converter (VSC) for the mitigation of various power quality issues in the PV-DSTATCOM. Traditional control algorithms guarantee faultless execution or outcomes only for a restricted range of operating situations due to their present design. Alternative regulators depend on more resilient neural network and fuzzy logic algorithms that may be programmed to operate in a variety of settings. In this study, an adaptive linear neural network (ADALINE) was proposed to solve the control problem more efficiently than the existing methods. The ADALINE method was simulated and the results were compared with the results of the synchronous reference frame theory (SRFT), improved linear sinusoidal tracer (ILST), and backpropagation (BP) algorithms. The simulation results showed that the proposed ADALINE method outperformed the compared algorithms. In addition, the total harmonic distortions (THDs) of the source current were estimated under ideal grid voltage conditions based on IEEE-929 and IEEE-519 guidelines. [ABSTRACT FROM AUTHOR]

This study investigated the problem of controlling a three-phase three-wire photovoltaic (PV)-type distribution static compensator (DSTATCOM). In order to model, simulate, and control the system, the MATLAB/SIMULINK tool was used. Different controllers were applied to create switching pulses for the IGBT-based voltage source converter (VSC) for the mitigation of various power quality issues in the PV-DSTATCOM. Traditional control algorithms guarantee faultless execution or outcomes only for a restricted range of operating situations due to their present design. Alternative regulators depend on more resilient neural network and fuzzy logic algorithms that may be programmed to operate in a variety of settings. In this study, an adaptive linear neural network (ADALINE) was proposed to solve the control problem more efficiently than the existing methods. The ADALINE method was simulated and the results were compared with the results of the synchronous reference frame theory (SRFT), improved linear sinusoidal tracer (ILST), and backpropagation (BP) algorithms. The simulation results showed that the proposed ADALINE method outperformed the compared algorithms. In addition, the total harmonic distortions (THDs) of the source current were estimated under ideal grid voltage conditions based on IEEE-929 and IEEE-519 guidelines.

In this paper, the effective utilization of single-phase self-excited induction generator (SEIG) with improved power quality features with the help of voltage source converter (VSC) is presented. This generator works as an isolated single-phase distributed power generation system. In this case, the frequency and voltage control of induction generator feeding linear/nonlinear load is carried out using VSC under different operating characteristics. The power quality issues such as reactive power compensation, voltage regulation, harmonics reduction have been addressed. The VSC is operated by the reference current extracted from the distorted load current using adaptive theory-based Improved Linear Sinusoidal Tracer (ILST) control strategy for effective utilization and power quality as per IEEE-519 standards. Nevertheless, the design procedure of the excitation capacitor for self-excitation of the generator is also presented in detail. The design performance of SEIG is also validated through finite element method (FEM). The entire system comprising induction generator, nonlinear/linear load, and VSC along with ILST control algorithm are modeled in MATLAB/Simulink Library. The simulation performance is carried out under various operating conditions and found satisfactory. Also, the system prototype is developed in the laboratory for real-time performance analysis and validation. The results obtained are found satisfactory as per standard. [ABSTRACT FROM AUTHOR]

This study presents a new control scheme for synchronisation of a photovoltaic (PV) system to a three‐phase grid employing self‐tuning concept. The proposed synchronisation approach is developed to mitigate load current harmonics, to extract maximum PV power and inject it into the grid. Maximum PV power is extracted using an incremental conductance‐based algorithm for estimating the reference DC‐link voltage of a voltage source inverter and a proportional–integral controller for regulating the actual DC‐link voltage. In the proposed self‐tuning filter (STF) algorithm, only a single module of STF is required to extract the fundamental components of load current and grid voltage thus reducing system size, complexity and overall response time. Simulation of the proposed system is carried out using MATLAB/Simulink followed by experimentation on a prototype of grid connected PV system developed in the authors' laboratory. The efficacy of the proposed STF control scheme is compared with a recently reported algorithm called improved linear sinusoidal tracer (ILST) and dual second‐order generalised integrator (DSOGI). From the comparison with DSOGI and ILST, it is observed that the STF‐based control scheme provides faster response and reduces total harmonic distortion of the grid current despite the distorted and unbalanced grid condition. [ABSTRACT FROM AUTHOR]

The power quality compensator chosen in this paper is a DSTATCOM which integrates a three phase four leg Voltage Source Converter (VSC) with a DC capacitor (Cdc). A single stage Solar Photovoltaic (SPV) system is implemented for maintaining the DC link voltage (Vdc) of the Shunt Active Power Filter (SAPF). The system proposed, improves the Power Quality (PQ) of the utility system as well as feeds the solar energy extracted into the system. Maximum Power Point Tracking (MPPT) is implemented to excerpt solar energy. The purpose of the proposed system is to mitigate harmonics and neutral current (Isn),to compensate reactive power and to maintain power factor near unity. The SAPF improves the PQ of the proposed system. The control algorithm proposed for SAPF is Neural Network (NN) based Improved Linear Sinusoidal Tracer (ILST) algorithm. An instantaneous compensation technique controls the variations in PV power of the SPV system with fast dynamic response. The proposed system is also compared with battery operated Boost Converter (BC) SPV fed SAPF system. The battery operated SPV system boost the DC link voltage and thus improves PQ throughout the day. The NN based control strategy and the SPV system are modeled, developed and validated in MATLAB SIMULINK. The simulation results justify the effectiveness of the propounded system. [ABSTRACT FROM AUTHOR]

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