Electrical Engineering最新文献

For control plants with an explicit model such as DC-DC converters, a system model-based control strategy which is an optimal composite of the plant model-based full state feedback (FSF) controller and the DC signal model-based integrator (I) is proposed. The proposed method can greatly simplify the control system design and significantly improve the control performance of the DC-DC converter systems. A simple universal design approach of the control strategy has been developed to synthesize the proposed FSF-I optimal composite controller. Compared with the plant model-free controllers such as PI and PID controllers, the FSF-I optimal composite controller is easier to design and tune, and also can achieve higher tracking accuracy, faster response, and better robustness. An application example of a 1 kW DC-DC converter is provided to verify the effectiveness of the proposed control strategy.

Accurate fault detection and monitoring are crucial for maintaining photovoltaic (PV) system performance. While previous studies mainly focused on PV system faults, they often lack a comprehensive approach to integrating advanced diagnostic techniques, leading to duplicated research efforts and insufficient exploration of novel methodologies. This paper investigates the use of the finite element method to simulate the electromechanical impedance technique for fault detection and classification in PV systems. A 3D finite element model of a photovoltaic panel was created using ANSYS software to understand the basics of this technique. Studies on different locations of structural cracks were conducted to assess their impact on PV system output. For model verification, various fault and normal state simulation datasets were collected, normalized using data from piezoelectric sensors, and preprocessed. These datasets were then fed into an extreme learning machine (ELM) algorithm designed to predict and classify damage locations. The results highlight the superior efficacy of the ELM algorithm in defect detection, boasting an impressive overall accuracy rate of 85%.

With the performance of soft magnetic composite (SMC) improves, there is a trend to develop permanent magnet claw pole machine (PMCPM) by using SMC cores in the past decades, as it is with complex 3D magnetic flux path. The traditional PMCPM (TPMCPM) needs to form the three phase operation by stacking three single phase modules in the axial direction, and each of them needs to be shifted with 120 degrees electrically to each other. In this paper, a PMCPM with concentrated winding (CWCPM) is proposed to overcome above constraints of the TPMCPM. Furthermore, the shielding layer is employed for reducing the flux leakage of CWCPM, and thus the performance of SL-CWCPM is improved. Considering these machines are with many design parameters, the multilevel sequential Taguchi method is employed and the sensitivity method with correction coefficient is employed for divide these design parameters into three groups. Lastly, the hybrid silicon sheet and SMC cores are employed to increase the performance of CWCPM, and the concept of the hybrid material magnetic core for the PMCPM is verified by the experiment.

The growing integration of photovoltaic (PV) power into the grid has brought on challenges related to grid stability, with the boost converter and the inverter introducing harmonics and instability, especially under non-linear loads and environmental changes. Therefore, conducting practical testing on grid-connected PV systems under various conditions can be difficult and often impossible due to the destructive nature of many scenarios. Existing research often lacks comprehensive modeling, real-world validation, and explicit adherence to grid connection standards. Thus, this paper aims to present a detailed modeling, design, and control strategy for a grid-connected PV system that accurately reflects the behavior of the 15-megawatt-peak (MW_p) PV plant at Oued El Kebrit, Algeria, while adhering to the IEEE 929–2000 and European EN 50160 grid connection standards. The developed one-megawatt model encompasses all components of the double-stage topology, namely the PV array, boost converter, maximum power point tracking (MPPT) controller, three-phase pulse width modulation (PWM), voltage source inverter (VSI), LCL filter, grid synchronization technique with a phase-locked loop (PLL), VSI dual-loop current controller with PI regulators, and other grid connection components. The entire proposed model, implemented in MATLAB/Simulink, was used to simulate various scenarios under different weather conditions, including standard test conditions (STC), a sudden drop in solar irradiation, and a real-world scenario. The simulation and comparison outcomes with real-life data collected from the Oued El Kebrit PV plant showed close alignment with the performance of the actual PV plant; this not only validated the model’s reliability and efficiency but also confirmed its compliance with IEEE and EN standards.

To realize the accurate determination of the partial discharge development stage of an oil-immersed power transformer by a method based on sparse decomposition is proposed in the insulation system of an oil-immersed power transformer. First, statistical parameters are extracted from the PD pulse signals to construct a complete atomic library. According to the sparse representation principle, the preliminary determination of the discharge stage can be realized by ignoring the influence of aging; secondly, to solve the influence of feature parameter correlation on the determination result, the statistical feature parameters are sparsely reconstructed to realize the ordering of the effectiveness of the statistical feature parameters; lastly, to take into account the influence of the aging of the insulating cardboard, the plausible weights of the aging factor are calculated, and the number of votes for the development stage of partial discharge is determined according to the Borda voting mechanism. Two typical discharge defect models are designed so that the PDs on which the work focuses are superficial, and the measured signals are used to verify the validity of this paper's method. The results show that the highest accuracy is 56.2% when ignoring the influence of insulation cardboard aging, and the accuracy of the decision is less than 75% when considering the influence of aging without sparse reconstruction of statistical feature parameters; the method in this paper has good recognition effect, and the recognition accuracy is improved by 38.6% compared with that of ignoring the influence of aging of the insulation cardboard, and the average can reach 94.2%.

The significant growth in the utilization of electric vehicles (EVs) and adoption of distributed energy resources (DERs) have transformed the landscape of the energy sector. Despite the advantages offered by EVs and DERs, they introduce additional challenges to system operators. Therefore, this paper proposes a multi-objective optimization strategy for enhancing network operation and planning, focusing on the allocation and sizing of electric vehicle charging stations (EVCSs), DERs, and capacitor banks (CBs), along with dynamic network reconfiguration. For this purpose, a novel two-stage methodology is introduced to address planning and operation separately, in which decision variables that remain constant over time (e.g., location of DERs, EVCSs, and CBs) are distinguished from those that can be adjusted in real-time (e.g., network configuration, CB taps, and DER operating points). The main objective is to minimize overall costs, voltage deviation, and power losses while ensuring compliance with network constraints. The optimization problem is addressed through the multi-objective cuckoo search, and the final solution is chosen using the fuzzy decision-making method. Finally, the effectiveness of the proposed approach is demonstrated through a comprehensive comparison with prior studies in the field and with the well-established non-dominated sorting genetic algorithm II.

Abstract

Microgrids serve an essential role in the smart grid infrastructure, facilitating the seamless integration of distributed energy resources and supporting the increased adoption of renewable energy sources to satisfy the growing demand for sustainable energy solutions. This paper presents an application of integral reinforcement learning (IRL) algorithm-based adaptive optimal control strategy for automatic generation control of an is-landed micro-grid. This algorithm is a model-free actor-critic method that learns the critic parameters using the recursive least square method. The actor is straightforward and evaluates the action from the critic directly. The robustness of the proposed control technique is investigated under various uncertainties arising from parameter uncertainty, electric vehicle (EV) aggregator, and renewable energy sources. This study incorporates case studies and comparative analyses to demonstrate the control performance of the proposed control strategy. The effectiveness of the technique is evaluated by comparing it with deep Q-learning (DQN) control techniques and PI controllers. The proposed controller significantly improves performance metrics compared to the DQN and PI controllers. It reduces the peak frequency deviation by 6(%) and 14(%), respectively, compared to the DQN and PI controllers. When subjected to multiple-step load disturbances, the proposed controller reduces the mean square error by 28(%) and 42(%), respectively, while lowering both the integral absolute error and the integral time absolute error by 21(%) and 35(%) compared to the DQN and PI controllers. Additionally, when operating with renewable energy sources, the proposed controller decreases the standard deviation in the frequency deviation by 17(%) compared to the DQN controller and 23(%) compared to the PI controller.

Graphical abstract

The modular multilevel grid following string inverter (MMGFSI) has gained popularity in large rooftop solar photovoltaic power (PV) plant applications, with grid-integrated net metering facility. The performance of the standard PI controller-based MMGFSIs during grid load disturbances is not satisfactory due to the wide ripples, low dynamic performance, and low steady-state precision of the inverter current feedback regulation. This study proposes a repetitive control proportional-integral (RCPI) controller approach for the cascaded H-bridge (CHB) five-level grid following inverter to synchronize with the grid and satisfy enhanced power quality standards IEEE519 for large rooftop solar PV plant application system. Additionally, this proposed control topology performance has compared to PI controller-based MMGFSI’s and repetitive controller cascaded PI controller-based MMGFSI’s system. The proposed RCPI MMGFSI’s system performance has been tested on a PSIM simulation environment on a grid-connected, photovoltaic (PV) system with a diversity of linear and nonlinear load disturbances to show the viability and resilience of the suggested repetitive control strategy in practice. To provide the necessary carrier control signal for the sinusoidal pulse width modulation block (SPWM), a cycle delay has introduced in the RCPI feedback path. As a result, reducing grid-side harmonic distortion lowers the cost of the LCL filter connected to the inverter output This RCPI-based MMGFSI has 4.1 percent less overall harmonic distortion than the conventional PI controller-based MMGFSI and 0.21 percent less total harmonic distorted than repetitive cascaded PI controller-based MMGFSI’s system. Additionally, a hardware prototype has RCPI controller MMGFSI’s implemented to evaluate the five-level CHB MLI structure and switching topology.

This paper proposes a novel approach to assess network conditions, enabling timely decisions to be made regarding protective actions or control adjustments for distributed generators (DGs) instead of immediate disconnection upon detecting an unplanned islanding event. By facilitating swift decision-making, this strategy aims to minimize outage durations, enhance system reliability, and improve customer satisfaction levels. The first step in the proposed approach involves the implementation of a passive islanding detection method based on continuous monitoring of voltage and current phasors at DG buses equipped by micro-phasor measurement units (μPMUs). Subsequently, a faulted line detection algorithm is applied to identify if the fault lies within the isolated area. If the fault determined to be within the separated region, the DG disconnects from the grid, providing power solely to its local load. In contrast, if the fault is located outside the isolated area or if islanding occurs due to reasons other than faults, the DGs control strategies are adjusted to support the islanded conditions effectively. The performance of the proposed procedure is thoroughly analyzed through the integration of MATLAB and DIgSILENT simulation environments. The IEEE 33-bus and IEEE 69-bus test systems with both synchronous-based and inverter-based DGs are used for the assessment.

This paper focuses on the investigation of conducted electromagnetic interference (EMI) in a two-level onboard charging system for electric vehicles. It analyzes the mechanisms and coupling paths of conducted EMI in the system, identifying that EMI primarily originates from the switching actions of power components and propagates through transformers, passive components, and parasitic parameters of the circuit. Different models for various components are established using analytical methods, finite element numerical analysis methods, and measurement methods. Relevant parasitic parameters are extracted to construct a comprehensive system-level simulation and prediction model for conducted EMI. The accuracy of the simulation and prediction model is validated through EMI testing. To further predict the sources of conducted EMI in the system, an EMI node prediction method is proposed based on the simulation model. This method involves analyzing the frequency-domain simulation waveforms of EMI nodes within the charging system, identifying prominent nodes with significant EMI, and implementing relevant suppression measures for the components surrounding those nodes that contribute to EMI. The effectiveness of the proposed EMI node prediction method is verified through EMI suppression experiments.