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Using the Z-network idea and conventional isolated power converters as its foundation, this article introduces a multi-port converter. This article proposes a topology that is called Multi-Port Z-Network Converter (MPZNC). A grid-connected inverter can include N input sources into a single DC bus using the suggested topology. Bypassing the input and output circuits was another capability it possessed with the boost function. Compared to traditional converters, these one-use fewer parts to integrate various energy sources. Consequently, it has better circuit properties and achieves higher conversion efficiencies. In comparison to the standard Z-Source Converter (ZSC), it improves the input-output voltage transformation ratio and provides a wider voltage control range. The circuit design, operating principle, control mechanism, and simulation data have been presented to prove technically possible. To investigate the suggested MPZNC-fed Single Phase Five Level (SPFL) inverter in the given scenario, a 1.5 kW, 230 V, 50 Hz miniature laboratory study model was created. According to the findings, the suggested converter has an efficiency of around 93% and provides double the amount of boosting time as the standard ZSC converter.

Deep learning and digital image technologies have combined to create a potentially effective tool for identifying partial discharge (PD) patterns precisely. However, it is necessary to investigate which algorithm guarantees the best performance. The more common tools are restricted by a lack of training data and an advanced model in itself. Therefore, the main goal of this paper is to develop an efficient hybrid network comprising two deep networks, long short-term memory (LSTM), and convolutional neural network (CNN), for identifying the form of PD. A total of $8186\times 25$ (non-PD $\times $ PD) images were applied to assess the proposed methods. The size of the PD type was increased to 3675 images using data augmentation techniques. The results indicated that the integration of CNN and LSTM networks can provide a more robust implementation for PD detection. The integrated CNN-LSTM deep network based on data augmentation outperformed features derived from a single deep network. The recall, F-measure, and classification precision have 99.9% as a validation accuracy with a 99.8% intersection over union and a loss of 0.004.

Nowadays, renewable energy sources such as solar Photovoltaic (PV) and wind power systems play a significant part in the process of generating electricity. These systems are tremendously reliant on the present and forecasted weather conditions. The inconsistent behavior of the system has an impact on the production as well as the variances in the output. As a result, the requirement for energy transmission and distribution systems to compensate promptly is becoming crucial. This paper proposes a new smart grid application for power system operation. A Static Compensator (STATCOM) device is utilized to improve the power quality and power flow in the distribution system, reduce unwanted harmonics and compensate for reactive power in the power sources. A quasi-Z-Source Inverter (qZSI) based STATCOM is constructed in connection with a Three-Phase Four-Wire (3P4W) distribution system. The combination of a q Z-source and a PV system makes up the suggested compensator circuit that is employed here for switching. To control the suggested compensator, an Adaptive Frequency Fixed (AFF) - Second Order Generalized Integrator (SOGI) has been implemented. The Fuzzy Logic Controller (FLC) is used to optimize the parameters of the Proportional Integral (PI) controller, such as $K\text{p}$ and $K\text{i}$ . The experimental results demonstrate that the system effectively reduces the Total Harmonic Distortion (THD) of the source current, decreasing it from 25.5% to 1.3%. Moreover, the system has demonstrated its ability to supply active power to the load.

A new Modified Packed U-Cell (MPUC) converter architecture with cascading is proposed in this paper. To provide an output voltage of 25 levels, the proposed cascaded MPUC needs only twelve power switches and four power sources. The converter comprises two cascaded MPUCs with DC supply in a ratio of 5:1. One converter is operating at low frequency (LF) and the other at high frequency (HF) that leads to lower power losses and higher levels. Besides, a variable frequency method is anticipated to produce a 25-level output voltage which has low harmonic content (THD) with the help of Selective Harmonic Mitigation (SHM). The optimum switching angles for SHM are obtained through solving the SHM equations using the Genetic Algorithm (GA). The designed controller is efficient and suitable for applications that require low-frequency operation either in stand-alone or grid-tied. The proposed inverter and its operation procedure have been investigated using MATLAB®/Simulink software, and the findings demonstrate that the proposed inverter output voltage has reduced THD significantly. The simulation results are verified using the typhoon HIL-402 emulator. Also, the power loss analysis is done using PLECS. The maximum efficiency of the converter is found to be around 98.34%. The simulation results justified the efficiency and viability of low 25-level THD voltages.

Integrating renewable energy sources (RES), such as photovoltaic (PV) systems, into power system networks increases uncertainty, leading to practical challenges. Therefore, an accurate photovoltaic (PV) power prediction model is required to provide essential data that supports smooth power system operation. Hence, the work presented in this paper compares and discusses the results of different machine learning (ML) techniques in predicting the power produced by the 300 MW Sakaka PV Power Plant in the north of Saudi Arabia. The validation of the presented work is performed using real-world operational data obtained from the specified solar farm. Several performance measures, including accuracy, precision, recall, F1 Score, and mean square error (MSE), are used in this work to evaluate the performance of the different ML approaches and determine the most precise prediction model. The obtained results show that the Support Vector Machine (SVM) with a Radial basis function (RBF) is the most effective approach for optimizing solar power prediction in large-scale solar farms.

Static compensators (STATCOMs) are often used in distribution systems to enhance power quality. There is a need to enhance the performance of STATCOM to optimize its utilization and facilitate the provision of additional ancillary services. This paper employs the multilayer discrete noise-eliminating second order generalized integrator (MDNESOGI) to regulate the quasi-impedance source inverter (qZSI)-STATCOM for power exchange with the grid. Compared to conventional second-order generalized integrator (SOGI), MDNESOGI exhibits a higher capability for rejecting DC offset. In instances of abnormal grid operation or system malfunction, the inclusion of DC rejection capability enhances the robustness and reliability of the system. The suggested control algorithm only requires two integrators, three mathematical operators, and a damping factor, making it far easier to implement than transformation-based methods. The distorted load current is broken down into its active and reactive components using this control mechanism. The reference currents are then calculated by multiplying these parts by their corresponding voltage standards. The DC offset is reduced and transient oscillations in the weight component are eliminated by adjusting the damping factor. The suggested algorithm effectively handles power quality tasks like (a) reducing harmonic distortion, (b) compensating for reactive power, (c) adjusting for power factor, and (d) balancing the load under different conditions in the distribution system. The experimental study results are used to examine the stability of the proposed control scheme in both static and dynamic scenarios. In addition, a comparison to traditional methods is provided to demonstrate the new method’s superiority. Experimentation results show that the suggested controller is superior to its contemporaries in all scenarios where power quality is a factor, meeting the IEEE standard requirements.

In the literature, different approaches that are employed in designing automatic voltage regulators (AVRs) usually model the AVR as a single-input-single-output system, where the input is the generator reference voltage, and the output is the generator voltage. Alternately, it could be thought of as a double-input, single-output system, with the excitation voltage change serving as the additional input. In this paper, unlike in the existing literature, we designed the AVR system as a sextuple-input single-output (6ISO) system. The inputs in the model include the generator reference voltage, regulator signal change, exciter signal change, amplifier signal change, generator output signal change, and the sensor signal change. We also compared the generator voltage responses for various structural configurations and regulator parameter choices reported in the literature. The effectiveness of numerous controllers is investigated; the proportional, integral and differential (PID) controller, the PID with second-order derivative (PIDD2) controller, and the fractional order PID (FOPID) controller are the most prevalent types of controllers. The findings reveal that the regulator signal change and the generator output signal change significantly impact the generator voltage. Based on these findings, we propose a new approach to design the regulator parameter to enhance the response to generator reference voltage changes. This approach takes into consideration changes in the generator reference voltage as well as the regulator signal. We calculate the regulator settings using a cutting-edge hybrid technique called the Particle Swarm Optimization African Vultures Optimization algorithm (PSO–AVOA). The effectiveness of the regulator design technique and the proposed optimization algorithm are demonstrated.

The standalone microgrids with renewable energy resources (RERs) such as a photovoltaic (PV) system and fast changing loads face major challenges in terms of reliability and power management due to a lack of inherent inertial support from RERs and their intermittent nature. Thus, energy storage technologies such as battery energy storage (BES) are typically used to mitigate the power fluctuations and maintain a power balance in the system. This paper presents a model predictive control (MPC) based power management strategy (PMS) for such standalone PV/battery systems. The proposed method is equipped with an autoregressive integrated moving average (ARIMA) prediction method to forecast the load and environmental parameters. The proposed controller has the capabilities of (1) effective power management, (2) minimization of transients during disturbances, and (3) automatic switching of the operation of the PV between the maximum power point tracking (MPPT) mode and power-curtailed mode that prevents the overcharging of the battery and at the same time maximize the PV utilization. The effectiveness of the proposed method has been verified through a comprehensive simulation-based analysis.