This paper introduces an advanced approach to achieve real and reactive power control in grid-connected three-phase inverters under unbalanced grid conditions. A novel Proportional-Integral-Resonant (PIR) control strategy is proposed, aiming to address two primary objectives: maintaining balanced grid currents and point of common coupling voltages (PCC), while simultaneously injecting the grid with the desired real and reactive power. Comparative analyses with conventional Proportional-Integral (PI) and Proportional-Resonant (PR) controllers are conducted. The PI controller exhibits a better transient response and lower steady-state error compared to the PR controller. However, under unbalanced grid conditions, the PI controller exhibits oscillations in the real and reactive power output. In contrast, the PR controller effectively manages unbalanced situations and maintains power control. Thus, the proposed PIR method combines the advantages of both PI and PR controllers: superior transient response and lower steady-state error of PI, and effective handling of unbalanced conditions and power control of PR. The simulation and Hardwre’s experimental validation confirm the efficacy of the proposed method.

This study investigates the reduction of torque ripple in Permanent Magnet Synchronous Motors (PMSMs) during open-circuit fault (OCF) conditions using an adaptive Proportional-Integral-Resonant (PIR) control approach. Torque ripples, commonly exacerbated by faults in motor operation, result in undesirable vibrations, noise, and mechanical stress, thereby impacting the motor’s performance and longevity. The proposed PIR control method integrates a resonant controller with a conventional PI-current controller to enhance torque ripple suppression during OCF operation. Experimental validation, conducted using a dSPACE DS1104 platform, focuses on reducing torque ripple during OCF events. The results show that the adaptive PIR control method significantly reduces torque ripples, thus promoting smoother motor operation, increased efficiency, and increased reliability under fault conditions.

Model predictive control (MPC) has gained considerable attention in power electronics but the high computational demands preclude its embedded and real-time implementation. This paper presents a novel FPGA-based hardware acceleration approach for MPC implementation in power electronic converters, enabling real-time, embedded operation with a total processing time potentially less than 1 µs, effectively eliminating the computational bottleneck. Furthermore, a comprehensive design guideline of FPGA-based hardware acceleration is presented using a buck converter as example, including top architecture programming to bottom calculation kernels design. This FPGA-based hardware acceleration framework for MPC is versatile, offering potential applicability across a wide range of power electronic systems. Simulation and experimental results using a buck converter with 5-step prediction horizon validate the efficacy of this approach.

Nonintrusive load monitoring (NILM) is to obtain individual appliance’s electricity consumption from aggregated smart meter data. In this article, we propose a middle window transformer model, termedMidformer, for NILM. Existing models are limited by high computational complexity, dependency on data, and poor transferability. In Midformer, we first exploit patchwise embedding to shorten the input length, and then reduce the size of queries in the attention layer by only using global attention on a few selected input locations at the center of the window to capture the global context. The cyclically shifted window technique is used to preserve connection across patches. We also follow the pretraining and fine-tuning paradigm to relieve the dependency on data, reduce the computation in modeling training, and enhance transferability of the model to unknown tasks and domains. Our experimental study using two real-world data sets demonstrates the superior performance and transferability of Midformer over three baseline models.

With development of the photovoltaic industry, solar power generation forecasting using weather data has become an important problem. Various machine learning (ML) algorithms have been proposed to handle the random and massive weather data, with considerable recent interest on deep neural networks (DNN). Recent studies show that DNNs are vulnerable to adversarial examples, but most prior work has focused on their impact on the classification problem. In this paper, we investigate the problem of adversarial attacks on solar power generation forecasting, which is a regression problem. We examine the impact of adversarial attacks on both the DNN model and a LASSO-based statistical model proposed in our prior work. Both white-box attack and black-box attack are examined, along with the effect of adversarial training.

The three-phase grid system can become unbalanced for reasons such as single-phase loads and single-phase renewable energy sources connected to the grid. This unbalance not only affects inverter operation, but also impacts other loads connected to the grid. The traditional control methods for three-phase grid-connected inverter operation such as the proportional-integral (PI) control and proportional-resonant (PR) control have been used for balanced system conditions. The technique proposed here utilizes a PR controller to balance the grid currents while simultaneously injecting a commanded power into the grid. Simulation and experimental results confirm the effectiveness of this control method.

Presented in this paper is a method to use a three-phase inverter to inject currents to balance the grid currents while supplying power to the grid. The reference currents for the inverter are determined from the negative/zero sequence components of the measured load currents using a time-domain symmetrical components extraction method. Under the existence of unbalanced conditions, the proposed method corrects the unbalanced grid currents while simultaneously supplying the commanded power to the grid. The simulation and experimental results validate the performance of this proposed method.

As a type of inverter-based generation, an adjustable-speed pumped storage hydropower (AS-PSH) plant is a functional extension of conventional PSH plants and can provide more flexible and faster responses to provide ancillary services than conventional synchronous generators, thereby making a positive impact on power system voltage stability. This paper proposes a coordinated voltage control scheme of an AS-PSH plant with a wind power plant (WPP). The coordinated controller dispatches partial voltage set points, which are determined in proportion to the reactive current capability of each unit; thus, the controller enables the unit with a larger capability to receive a larger voltage set point so that the coordinating controller enhances the voltage controllability of the units. In addition, to stabilize the voltage at the point of interconnection after a fault clearance, reactive current initialization schemes are implemented in the AS-PSH and WPP controllers. The initialization schemes detect a sudden voltage rise (indicating the fault clearance) and enable the AS-PSH and WPP to rapidly reduce reactive current injections to the power system. The performance of the proposed scheme was tested for small and large voltage dips using PSCAD.

The smart grid (SG) has emerged as an important form of the Internet of Things. Despite the high promises of renewable energy in the SG, it brings about great challenges to the existing power grid due to its nature of intermittent and uncontrollable generation. In order to fully harvest its potential, accurate forecasting of renewable power generation is indispensable for effective power management. In this paper, we propose a least absolute shrinkage and selection operator (LASSO)-based forecasting model and algorithm for solar power generation forecasting. We compare the proposed scheme with two representative schemes with three real world datasets. We find that the LASSO-based algorithm achieves a considerably higher accuracy comparing to the existing methods, using fewer training data, and being robust to anomaly data points in the training data, and its variable selection capability also offers a convenient tradeoff between complexity and accuracy, which all make the proposed LASSO-based approach a highly competitive solution to forecasting of solar power generation.

Energy management is indispensable in the smart grid, which integrates more renewable energy resources, such as solar and wind. Because of the intermittent power generation from these resources, precise power forecasting has become crucial to achieve efficient energy management. In this paper, we propose a novel adaptive learning hybrid model (ALHM) for precise solar intensity forecasting based on meteorological data. We first present a time-varying multiple linear model (TMLM) to capture the linear and dynamic property of the data. We then construct simultaneous confidence bands for variable selection. Next, we apply the genetic algorithm back propagation neural network (GABP) to learn the nonlinear relationships in the data. We further propose ALHM by integrating TMLM, GABP, and the adaptive learning online hybrid algorithm. The proposed ALHM captures the linear, temporal, and nonlinear relationships in the data, and keeps improving the predicting performance adaptively online as more data are collected. Simulation results show that ALHM outperforms several benchmarks in both short-term and long-term solar intensity forecasting.

Despite the high promises of renewable energy, it brings great challenges to the existing power grid due to its nature of intermittent and uncontrollable generation. In order to fully harvest its potential, accurate forecasting of renewable power generation is indispensable for effective power management. In this paper, we propose a LASSO- based forecasting model and algorithm for solar power generation forecasting. We compare the proposed scheme with two representative schemes with a real world dataset. We find that the LASSO-based algorithm achieves a considerably higher accuracy comparing to the existing methods, using fewer training data and being robust to anomaly data points in the training data. Its variable selection capability also offers a trade-off between complexity and accuracy, which all make it a highly competitive solution to forecasting of solar power generation.

Presented in this paper is grid current observer-based compensation control for a single-phase grid-connected inverter that is connected to a weak grid. Traditional single-loop PI current control performance suffers in the presence of the grid voltage distortion and grid impedance uncertainty. To compensate for disturbances from the grid side, two different compensation control structures are proposed. One is a feed forward method, which uses estimated grid current as a feed forward signal. The other one is a modified disturbance observer method, which compensates for the estimated disturbance. Disturbance rejection ability can be improved by adopting disturbance compensation. Simulation and experiments have been carried out on a 1 kW single-phase grid-connected inverter and show that the proposed controller can achieve low total harmonic distortion (THD) and robustness to grid impedance variation.

Although considerable advances have been made in single microgrid (MG) systems, the problem of cooperation among MGs and the macrogrid has attracted considerable interest only recently. As in wireless communications systems, exploiting the temporal, spatial, and technological diversities in multiple cooperative MGs could bring about more efficient power generation and distribution. This paper investigates a hierarchical power scheduling approach to optimally manage power trading, storage, and distribution in a smart power grid with a macrogrid and cooperative MGs. We first formulate the problem as a convex optimization problem and then decompose it into a two-tier formulation. The first-tier problem jointly considers user utility, transmission cost, and grid load variance, while the second-tier problem minimizes the power generation and transmission cost, and exploits distributed storage in the MGs. We develop an effective online algorithm to solve the first-tier problem and prove its asymptotic optimality, as well as a distributed optimal algorithm for solving the second-tier problem. The proposed algorithms are evaluated with trace-driven simulations and are shown to outperform several existing schemes with considerable gains.

Proposed in this paper are three different implementations for digital average current mode control for DC-DC converters operating in the continuous conduction mode. The advantages and disadvantages of each implementation are described. Design procedures for the both the voltage and current loops are described. Using a boost converter prototype, the dynamic performance of all three implementations has been evaluated and is presented here.

As advances are made in single Microgrid (MG) systems, the problem of cooperation among MGs and the Macrogrid has attracted considerable interest only recently. As in wireless communications systems, exploiting the temporal, spatial, and technological diversities in multiple cooperative MGs could bring about more efficient power generation and distribution. This paper investigates a hierarchical power scheduling approach to optimally manage the power trading, storage and distribution in a smart power grid with a Macrogrid and cooperative MGs. We first present a two-tier formulation: the first-tier problem jointly considers user utility, transmission cost, and grid load variance, while the second-tier problem minimizes the power generation and transmission cost and exploits distributed storage in the MGs. We develop an effective online algorithm to solve the first-tier problem and prove its asymptotic optimality, and develop a distributed optimal algorithm for solving the second-tier problem. The proposed hierarchical power scheduling algorithms are evaluated with trace-driven simulations and are shown to outperform several existing schemes with considerable gains.

In recent years, the smart grid has been recognized as an important form of the Internet of Things (IoT). The two-way energy and information flows in a smart gird, together with the smart devices, bring about new perspectives to energy management. This paper investigates a distributed online algorithm for electricity distribution in a smart grid environment. We first present a formulation that captures the key design factors such as user's utility, grid load smoothing, and energy provisioning cost. The problem is shown to be convex and can be solved with a centralized online algorithm that only requires present information about users and the grid in our prior work. In this paper, we develop a distributed online algorithm that decomposes and solves the online problem in a distributed manner, and prove that the distributed online solution is asymptotically optimal. The proposed distributed online algorithm is also practical and mitigates the user privacy issue by not sharing user utility functions. It is evaluated with trace-driven simulations and shown to outperform a benchmark scheme.

The two-way energy and information flows in a smart gird, together with the smart devices, bring new perspectives to energy management and demand response. This paper investigates a distributed online algorithm for electricity energy distribution in a smart grid environment. We first present a formulation that captures the key design factors such as user utility, grid load smoothing, and energy provisioning cost. The problem is shown to be convex and can be solved with an online algorithm that only requires present information about users and the grid in our prior work. In this paper, we develop a distributed online algorithm which decomposes and solves the online problem in a distributed manner, and prove that the distributed online solution is asymptotically optimal. The proposed distributed online algorithm is also practical and effective for user privacy protection. It is evaluated with trace-driven simulations and shown to outperform a benchmark scheme.

Microgrid (MG) is a promising component for future smart grid (SG) deployment. The balance of supply and demand of electric energy is one of the most important requirements of MG management. In this paper, we present a novel framework for smart energy management based on the concept of quality-of-service in electricity (QoSE). Specifically, the resident electricity demand is classified into basic usage and quality usage. The basic usage is always guaranteed by the MG, while the quality usage is controlled based on the MG state. The microgrid control center (MGCC) aims to minimize the MG operation cost and maintain the outage probability of quality usage, i.e., QoSE, below a target value, by scheduling electricity among renewable energy resources, energy storage systems, and macrogrid. The problem is formulated as a constrained stochastic programming problem. The Lyapunov optimization technique is then applied to derive an adaptive electricity scheduling algorithm by introducing the QoSE virtual queues and energy storage virtual queues. The proposed algorithm is an online algorithm since it does not require any statistics and future knowledge of the electricity supply, demand and price processes. We derive several "hard" performance bounds for the proposed algorithm, and evaluate its performance with trace-driven simulations. The simulation results demonstrate the efficacy of the proposed electricity scheduling algorithm.