Behind-the-meter photovoltaic battery systems exhibit heterogeneous daily operating behaviors shaped by weather conditions, household demand, storage control, and grid interaction. Identifying these operational regimes is essential for improving forecasting, system control, and policy design, particularly where labeled operational data are unavailable. This study proposes an unsupervised framework to identify recurring daily operational regimes in a residential photovoltaic battery system and to quantify regime-specific weather sensitivities. Fourteen months of high-resolution operational data from a residential photovoltaic battery system in Yangon, Myanmar, are analyzed. Daily features capturing photovoltaic generation, electricity consumption, grid exchange, battery charging and discharging, state of charge, and self-consumption are derived from intraday profiles. Isolation Forest outlier detection removes anomalous operating days, yielding 411 representative days for analysis. K-means clustering identifies four operational regimes (Silhouette = 0.216), validated using Gaussian Mixture Models, hierarchical clustering, and density-based methods. Principal component analysis confirms consistent and interpretable operational structure and interpretability of the identified regimes. Cluster-wise standardized regression models link daily photovoltaic energy production to meteorological drivers. Solar irradiance is the dominant explanatory variable across all regimes, with effect sizes increasing from grid-dependent to high PV self-sufficient modes. Temperature and relative humidity exhibit regime-dependent secondary effects, while wind speed plays a limited role. Seasonal analysis reveals systematic shifts in regime prevalence driven by climatic conditions. The proposed framework provides an interpretable approach applicable to tropical monsoon PV-battery systems for diagnosing real-world photovoltaic battery system behavior and yields actionable regime labels that support regime-aware forecasting and adaptive energy management in distributed energy systems.

High efficiency is a primary goal for photovoltaic (PV) inverters. Optimization procedures followed by PV inverter designers result in static circuit designs, with respective efficiency curves that exhibit their maxima in a narrow range of PV array voltage and power. Nevertheless, the individual PV array characteristics, the solar irradiance and ambient temperature conditions with their daily and seasonal variations, as well as possible partial shading of the PV array, normally shift the operating point of PV inverters away from their maximum-efficiency range. This paper proposes a dynamic self-reconfiguration method for transformerless Buck-Boost PV inverters. The proposed method involves extending the PV inverter’s power circuit with appropriately selected components and reconfiguring its structure and switching frequency in real-time, so that its efficiency is maximized under any operating conditions. The experimental results obtained using a reconfigurable Buck-Boost PV inverter prototype demonstrate that by applying the proposed technique, the PV inverter’s energy efficiency can be increased by 1.5%.

Multi-port power converters enable the combination of renewable energy sources and energy storage. This paper presents a single-phase standalone multi-port inverter (MPI) that integrates a photovoltaic (PV) array, a battery storage unit, a supercapacitor (SC) bank, and electric vehicle (EV) battery. The proposed MPI regulates the power flow between these ports to ensure optimal energy management and reliable operation of the AC power-supply system. The PV array is directly connected to the DC-link, eliminating the need for an additional power converter and reducing the volume and cost of the overall MPI. The energy management algorithm is designed to independently control each port and maintain a power balance between the input and output of the proposed MPI. Compared to conventional MPI architectures, the multi-port DC/AC inverter proposed in this paper contains less power devices, resulting in reduced switching power losses and implementation cost. Experimental results demonstrate the effectiveness of the proposed system in regulating each port independently under various power flow scenarios, with a maximum efficiency of 95.3%.

In grid-connected inverter systems, the three-phase asymmetric LCL (A-LCL) filter has outstanding characteristics of simple structure, almost the smallest total inductance, and the strong resistance to the adverse effects of parameter shifts. Nevertheless, the order of this kind of power filter is high, and the control model is complicated due to its asymmetric structure. In this paper, a simplified modeling method, which effectively reduces the system order is proposed, without affecting the control performance. Then, the Kalman filter estimation (KF-estimation) is adopted to reconstruct the PCC voltage. A 380 V/50 Hz/6 kW three-phase laboratory setup has been developed to verify the correctness and effectiveness of the proposed control strategy.

Power line communication (PLC) technological advancements are currently being led by the interoperation needs in applications such as smart grids, building energy management systems, electric vehicles, and the Internet of Things. In this paper, alternative control schemes are proposed and compared for the simultaneous transmission of power and digital data through the power circuits of the modules synthesizing a single-phase cascaded H-bridge multilevel inverter. The proposed PLC method does not require any additional circuit elements in the power loop for the PLC implementation. Special focus is paid to the control schemes applied to the H-bridge driving circuits to produce a low total harmonic distortion (THD) without compromising the reliability of data communication. Various parametric simulation models are designed and tested using time-division multiplexing with two variations over the assignment technique for the PLC communication task. The results suggest that the proposed low-bandwidth PLC design is robust enough to provide the required communication fidelity and capable of successfully transmitting the mixed-signal (i.e., power and data) through the power cable under various operating conditions.

In recent years, the deadbeat-control-based MPC algorithm (DBC-MPC) has been widely studied because it can effectively reduce the number of candidate vectors. However, for the LCL-type grid-connected inverter, when calculating the reference value of the inverter output voltage, the control algorithm only addresses the influence of the current tracking error on the reference value of the output voltage of the inverter, and ignores the influence of the filter capacitor voltage, resulting in poor control performance. Therefore, in this paper an improved DBC-MPC is proposed for the LCL-type grid-connected inverter, where both effects of current and voltage tracking errors on the control performance are fully considered. Compared with the traditional DBC-MPC algorithm, the control performance of the system is greatly improved, while still effectively reducing the number of candidate voltage vectors. Finally, a three-phase simulation model is built to verify the control performance of the proposed algorithm.

The power supply system design of a marine data buoy system is a challenge. Due to cost reasons, it is difficult for marine buoys to obtain electricity directly from the power grid. At present, marine buoys use solar energy, wind energy or wave energy to supply power to sensors. Among them, the employment of wave energy and solar energy has broad application prospects due to the complementary power production of these sources. This paper presents a new wave energy conversion (WEC) device for a marine buoy, which is composed of a floating body and a power generation device. In addition, a three-dimensional finite element simulation model of the buoy movement is established, and the simulation is carried out using hydrodynamic software. On the basis of simulation, experiments are carried out on a 50 kg core power generation unit to estimate the power generation performance of the proposed WEC device.

This paper presents an IGBT open-circuit fault diagnosis method based on Kalman filter model and reconfiguration algorithm for H6 grid-tied inverter. This method can detect open-circuit fault by only sampling the inductor current through Kalman filter model. Then, through the reconfiguration algorithm, the H6 grid-tied inverter is reconfigured as a Boost-type converter to identify the faulty IGBT device. The Kalman filter model can also predict the grid voltage provided to the control loop, thus saving the ac voltage sensor for the system. This method does not require extra sensors and diagnostic circuits, so it can be easily embedded in the DC/AC inverter system.

In grid-tied DC/AC inverter applications, the equivalent grid impedance often varies widely, which may limit the bandwidth of the inverter control system. This paper introduces a new method to perform the online estimation of the equivalent grid impedance with a Kalman filter by observing the grid voltage and grid current, as well as the voltage at the point of common coupling (PCC). With the estimated grid impedance, a high control bandwidth can be achieved for the grid-tied inverter through online regulation of the proportional coefficient of a current controller. A MATLAB/Simulink model of a grid-tied single-phase inverter has been setup to demonstrate the effectiveness of the proposed method. The simulation results show that the online estimated impedance is accurate enough and the inverter system can continuously maintain a high bandwidth, even under weak grid operating conditions.

Virtual oscillator control (VOC) has been proposed for Microgrids, since compared to the droop control method, VOC has a faster transient response. However, the output voltage of the conventional VOC always contains the third-harmonic. Thus, in the grid-connected mode, the third-harmonic voltage causes the generation of significant third-harmonic current which is injected into the power grid. In this paper, by analyzing the nonlinear oscillator and simplifying the nonlinear current source in the oscillator, a novel VOC for three-phase DC/AC inverter is proposed, where the third-harmonic of the oscillator output voltage can be successfully eliminated, whether in the islanded or grid-connected mode of operation. In addition, compared with the traditional VOC, the dynamic response of the proposed VOC-based inverter can be significantly improved, especially in the islanded mode. Experimental device designed on the DSPACE DS1202 is developed to verify the feasibility of the proposed strategy.

An extensive utilization of wireless sensor networks has evolved during the last years for monitoring various environmental and artificial processes. When operating in remote locations, the nodes of wireless sensor networks are typically power supplied by an energy production and management system, comprising low-power renewable energy sources, a power electronic converter, and a battery-based energy storage unit. In this paper, a methodology is proposed for optimally designing the energy production and processing system of a wireless sensor network node simultaneously at both the renewable power-supply system level and the power converter circuit level, through a unified design process. The impact of the objective function type on the power-supply design is also investigated in this paper. Design optimization and experimental results are presented, which demonstrate that the optimized power-supply structures derived by applying the proposed optimization technique exhibit lower cost of generated energy compared to partially optimized or totally nonoptimized structures and by that reduce the cost of the overall wireless sensor network node.

The installation of photovoltaic (PV) plants has been expanding rapidly across the world during the last years. In this paper, a methodology for the design optimization of PV plants is presented, which, in contrast to the conventional PV plant design approaches, is suitable to be executed using high time-resolution (i.e., 1-min-average) values of the meteorological input data. Due to the nonlinear operation of the devices comprising a PV plant, this allows for the accurate estimation of the PV plant performance during its operational lifetime period. A parallel processing-based implementation of genetic algorithms has been employed, which, compared to the serial execution, provides the ability to accomplish the proposed optimal design procedure in a considerably shorter time interval. The design optimization results confirm that the proposed method successfully accounts for both the meteorological conditions and the operational characteristics of the PV plant components and incorporates their impact on the PV plant energy production and cost in the design process. Thus, the proposed optimization method allows for optimum design of PV systems, which will provide maximum economic profit during their lifetime period.

The Sinusoidal Pulse Width Modulation (SPWM) principle is widely used in power electronic DC/AC converters (inverters) in energy conversion and motor control applications. The digital SPWM generation unit implementations have dominated over their counterparts based on analog circuits. In this paper, an FPGA-based SPWM generator is presented, which is capable to operate at switching frequencies up to 1 MHz (requiring FPGA operation at 100-160 MHz), thus it is capable to support the high switching frequency requirements of modern power electronic DC/AC converters. The successful operation of the proposed SPWM generator at high switching frequencies is demonstrated with post place-and-route simulation results for design verification and actual runs on hardware for design validation and proof-of-concept. The switching frequency of this system is an order of magnitude faster than previously proposed FPGA-implemented SPWM systems.