A Non-Isolated High Step-Up Interleaved DC-DC Converter with Diode-Capacitor Multiplier Cells and Dual Coupled Inductors

In this paper, a non-isolated high step-up dc-dc converter is presented. The proposed converter is composed of an interleaved structure and diode-capacitor multiplier cells for interfacing low-voltage renewable energy sources to high-voltage distribution buses. The aforementioned topology can provide a very high voltage gain due to employing the coupled inductors and the diode-capacitor cells. The coupled inductors are connected to the diode-capacitor multiplier cells to achieve the interleaved energy storage in the output side. Furthermore, the proposed topology provides continuous input current with low voltage stress on the power devices. The reverse recovery problem of the diodes is reduced. This topology can be operated at a reduced duty cycle by adjusting the turn ratio of the coupled inductors. Moreover, the performance comparison between the proposed topology and other converters are introduced. The design considerations operation principle, steady-state analysis, simulation results, and experimental verifications are presented. Therefore, a 500-W hardware prototype with an input voltage of 30-V and an output voltage of 1000-V is built to verify the performance and the theoretical analysis.Comment: 2020 North American Power Symposiu

A novel method for life estimation of power transformers using fuzzy logic systems: An intelligent predictive maintenance approach

Power transformers are a fundamental component of the modern power distribution network. The fault-free operation of step-up and step-down transformers is of prime importance to the continuous supply of electrical energy to the consumers. To ensure such efficient operation, power distribution companies carry out routine maintenance of distribution transformers through preplanned schedules. The efficacy of such maintenance depends on a proper understanding of the transformer and its components and efficient prediction of faults in these components. There are several components whose condition can be studied to predict transformer failures and therefore the overall health of a transformer. These include transformer windings, insulations, transformer oil, core insulations, and ferromagnetic cores. This work develops a new, simplified fuzzy logic-based method to predict the health of a transformer by taking into account the state of several individual components. Case studies are used to demonstrate the efficacy of the developed method

Robust Tilt-Integral-Derivative Controllers for Fractional-Order Interval Systems

In this study, an innovative and sophisticated graphical tuning approach is postulated, aimed at the design of tilt-integral-derivative (TID) controllers that are specifically customized for fractional-order interval plants, whose numerators and denominators consist of fractional-order polynomials that are subjected to parametric uncertainties. By leveraging the powerful value set concept and the advanced D-composition technique, a comprehensive set of stabilizing TID controllers is obtained. The validity and effectiveness of the proposed methodology are demonstrated by some examples, which vividly illustrate its remarkable performance and potential

Deep Reinforcement Learning Based Control of a Grid Connected Inverter With LCL-Filter for Renewable Solar Applications

This research paper presents a novel approach to current control in Grid-Connected Inverters (GCI) using Deep Reinforcement Learning (DRL) based Twin Delayed Deep Deterministic Policy Gradient (TD3) method. The study focuses on addressing the limitations of traditional control techniques and state of the art techniques, particularly Proportional-Integral (PI) control and Model Predictive Control (MPC), by leveraging the adaptive and autonomous learning capabilities of DRL. The proposed novel modified TD3-based DRL method learns an optimal control policy directly from raw data, enabling the controller to adapt and improve its performance in real-time. The research includes a comprehensive analysis of the implementation and validation of the modified TD3-based DRL control in a grid-connected three phase three level Neutral Point Clamped (NPC) inverter system with Inductor-Capacitor-Inductor (LCL) filter. Real-time validation experiments are conducted to evaluate the control performance, power transfer capability in grid compliance. Furthermore, a detailed comparison is presented with experimentation, highlighting the advantages of the TD3-based DRL control over PI and MPC control techniques. Robustness checking is performed under various operating conditions, including parameter variations and dynamic conditions in the grid. The results analysis demonstrates that the TD3-based DRL control outperforms traditional PI control techniques in terms of static, dynamic response, and robustness. Additionally, The DRL based grid connected inverter current control method is validated in Renewable Energy Source (RES) solar PV grid integration application

Optimal Allocation of Distributed Generators in Active Distribution Networks Using a New Oppositional Hybrid Sine Cosine Muted Differential Evolution Algorithm

The research proposes a new oppositional sine cosine muted differential evolution algorithm (O-SCMDEA) for the optimal allocation of distributed generators (OADG) in active power distribution networks. The suggested approach employs a hybridization of the classic differential evolution algorithm and the sine cosine algorithm in order to incorporate the exploitation and exploration capabilities of the differential evolution algorithm and the sine cosine algorithm, respectively. Further, the convergence speed of the proposed algorithm is accelerated through the judicious application of opposition-based learning. The OADG is solved by considering three separate mono-objectives (real power loss minimization, voltage deviation improvement and maximization of the voltage stability index) and a multi-objective framework combining the above three. OADG is also addressed for DGs operating at the unity power factor and lagging power factor while meeting the pragmatic operational requirements of the system. The suggested algorithm for multiple DG allocation is evaluated using a small test distribution network (33 bus) and two bigger test distribution networks (118 bus and 136 bus). The results are also compared to recent state-of-the-art metaheuristic techniques, demonstrating the superiority of the proposed method for solving OADG, particularly for large-scale distribution networks. Statistical analysis is also performed to showcase the genuineness and robustness of the obtained results. A post hoc analysis using Friedman–ANOVA and Wilcoxon signed-rank tests reveals that the results are of statistical significance

Enhancing Photovoltaic Conversion Efficiency With Model Predictive Control-Based Sensor-Reduced Maximum Power Point Tracking in Modified SEPIC Converters

The objective of this paper is to propose a new technique for maximum power point tracking (MPPT) in photovoltaic (PV) systems that utilizes fewer sensors, thereby reducing the hardware cost. The technique aims to achieve efficient MPPT under various environmental conditions by employing a modified SEPIC converter and a model predictive control (MPC)-based MPPT algorithm. To achieve the objective, the proposed technique utilizes only one voltage sensor and one current sensor, significantly reducing the hardware requirements compared to traditional MPPT techniques. The modified SEPIC converter is employed to regulate the voltage and current levels in the PV system. The MPC-based MPPT algorithm is implemented to dynamically adjust the operation of the converter and track the maximum power point. The algorithm incorporates a model predictive control approach, which utilizes a predictive model of the PV system to anticipate and optimize the power output. The algorithm predicts the behavior of the PV system based on the available sensor measurements, allowing for accurate MPPT. The algorithm operates in real-time, providing instantaneous adjustments to maximize power extraction. The study demonstrates that the proposed technique effectively tracks the maximum power point of the PV system using only one voltage sensor and one current sensor, thus reducing the overall hardware cost. The MPC-based MPPT algorithm, in combination with the modified SEPIC converter, achieves efficient power extraction under various operating conditions. The simulation and experimental results indicate that the proposed technique outperforms traditional MPPT techniques in terms of cost-effectiveness and power extraction efficiency