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This paper presents an effective resonance suppression control for grid-interactive inverters with LCL filter in weak grid conditions. The resonance suppression mechanism is based on an adaptive model predictive control (AMPC). The large parasitic impedance and low SCR of weak grid challenges the operation of grid-interactive inverters. Specifically, the LCL filter resonance may get excited, resulting in collapse of the inverter operation. The proposed AMPC scheme autonomously alter the controller objectives for suppressing the resonance. The proposed control scheme alters the feedback currents based on the grid-condition. During the stiff grid conditions, grid current serves as feedback to the controller. While in the weak grid conditions, the inverter current is selected as feedback signal. The toggling action between these two feedback currents is determined by comparing moving RMS of grid current with desired current as constraint in the proposed AMPC cost function. The performance of proposed controller is verified experimentally. The results demonstrate that the proposed controller features robust performance under various grid conditions, model parameter mismatch, seamless transition between controller modes of operation and feedback alteration to attain resonance suppression, eliminates required tuning efforts in comparison to classical multi-nested loop control schemes, and provides high power quality.

This article proposes an optimal current control technique with switching event minimization for grid-interactive cascaded multilevel inverters (CMI) interfaced with battery energy storage sources. The proposed control scheme enables power-balancing functionality of battery cells, realizing optimal smart operation of CMI. Model predictive control (MPC) is known as a potential approach for multiobjective control schemes in single-loop manner for power electronics interfaces. However, MPC schemes are suffering from high computational burden that is magnified in topologies like the CMI, which have a substantial number of redundant control actions. The proposed control scheme utilizes a dynamic lookup matrix as an internal optimizer tool. The redundant switching sequences are cycled to equalize the power drawn from the independent battery energy storage sources while achieving a minimum energy control. The theoretical analysis and experimental case studies verify robustness and computational efficiency of the proposed multicriteria optimal controller with similar objective tracking when compared to finite-set MPC.

This article presents a hierarchical finite-set model predictive control (MPC) scheme to enable autonomous operation and self-balancing cascaded multilevel inverter. The proposed approach is an alternative to MPC scheme based on a generic cost function, which in some applications is ill fit or challenging to design. The proposed controller has a hierarchical framework to eliminate the overall cost function optimization and associated weight factor design stage of the control objectives. The control formulation approach allows for multiobjective optimization with a cost-tolerance framework. The concept is well suited to simplify the control design stage of cascaded H-bridge inverters at the grid-edge with advanced functionality. The control scheme achieves active and reactive power control with switching event reduction while equalizing power draw from the independent voltage sources. The latter of these objectives is made possible by the proposed hierarchical approach to the control objective tracking. The control is modularized for each phase, making the system robust to unbalanced grid conditions. The concept is explained in depth in simulation, and then tested experimentally on hardware.

This article presents an autonomous model predictive controlled smart photovoltaic (PV) inverter with proactive grid fault-ride through capability. The proposed smart inverter control features decoupled active and reactive power. It can seamlessly switch between the anticipated modes of operation based on grid condition or grid operator command. The smart inverter autonomously adjusts its active and reactive power set-points according to the grid condition, it operates in maximum power point tracking (MPPT) and low voltage ride through (LVRT) modes in normal grid and faulty grid condition modes, respectively. The proposed novel autonomous model predictive control (AMPC) scheme is leveraged to enhance the operation of the smart inverter. The AMPC includes online weight factor auto-tuning and control objective normalization to eliminate the required trial-and-error weight factor design stage in conventional model predictive control. This feature is particularly beneficial to the dual-mode smart inverter operation. The performance of the proposed grid-tied smart inverter based on the AMPC is verified experimentally. The results demonstrate that the proposed AMPC-based smart inverter features robust grid fault detection, autonomous adjustment of active and reactive power set-points, seamless transition between modes of operation, and elimination of controller tuning effort.

This paper presents a decoupled active and reactive power control scheme for grid-tied quasi-impedance source cascaded multilevel inverter (qZS-CMI). For photovoltaic (PV) applications, the proposed control scheme is based on an enhanced finite-set model predictive control (MPC) to harvest the desired active power from the PV modules with the ability to provide the ancillary services for the grid. The proposed control scheme has two modes of operation: normal grid mode and low voltage ride through (LVRT) mode. In normal grid mode, the controller commands the qZS-CMI to operate at the global maximum power point (MPP). The proposed technique regulates the impedance network's current and voltage according to the MPP of PV strings and grid current/voltage requirements. In LVRT mode, the controller commands the qZS-CMI to provide the required reactive power to the grid during voltage sags as an ancillary service from the inverter as imposed by the grid codes. The main features of the proposed system include the global MPP operation during normal grid condition, LVRT capability during a grid voltage sag, mitigation of the PV modules mismatch effect on overall energy harvesting, seamless transition between a normal grid and LVRT modes of operation, and an efficient predictive controller that exploits the model redundancies in the control objectives. Several real-time experiments are conducted to verify the system performance with transients in both the solar irradiance and the grid voltage.

This paper presents a control scheme for single-phase grid-following inverters that does not rely on the phase-locked-loop (PLL) algorithm for synchronizing the injected current with the grid. The absence of the PLL avoids unstable low damping modes that are possibly originated from the PLL’s nonlinear nature. Thereby, assuring the control scheme robustness, especially, in weak grid conditions. Furthermore, the proposed PLL-less control achieves tracking the commanded active and reactive power (PQ) set-point references in a single loop control scheme. Hence, reducing controller design complexity compared to conventional cascaded dual loop PQ control scheme. This work also illustrates the necessary conditions to assure that the control is asymptotically stable with weak resistive and inductive grids. Finally, several case studies are implemented to validate the theory developed.

This paper presents an observer based predictive control scheme for grid-interactive inverters with intrusion detection capability. The proposed framework is robust to potential cyber-physical attacks due to intentional electromagnetic interference (EMI) on the vulnerable Hall-effect current sensors of the inverter. The robust operation of smart inverters at the grid-edge is highly dependent on the reliable and accurate current measurement in their feedback control scheme. The proposed security framework highlights two layers; the first layer detects an intrusion by EMI attack on the current sensor on-board of the inverter, while the second layer supports the first layer by establishing a secure line of communication between the inverter and the supervisory controller by using a time-sensitive passcode key. The observer uses the voltage measurements, which are not susceptible to EMI attacks targeting current sensors. The smart inverter flags the compromised current sensors at the grid-edge in a proactive manner by evaluating the current sensor measurements and the observer output in a constraint penalty function – leveraging the model predictive control phenomena. The intrusion detection and controller performance has been verified by multiple case studies.

This paper presents an effective resonance suppression control when grid interactive inverter with LCL filter experience weak grid conditions. The resonance suppression mechanism is based on finite-set model predictive control (FS- MPC) with adaptive cost function to alter the controller objectives seamlessly. The large parasitic impedance and low short circuit ratio (SCR) of weak grid challenges the operation of grid- connected inverters. Specifically, the LCL filter resonance may get excited, resulting in collapse of the inverter operation. To address this issue, an effective active damping approach is implemented in the proposed control. In the proposed active damping scheme for resonance suppression, the feedback currents are switched based on the operating conditions of inverter. During the stiff grid conditions, grid current serves as feedback to the controller. While in the weak grid conditions, the inverter current is selected as feedback signal. The toggling action between these two feedback currents is determined by comparing moving RMS of grid current with threshold current as constraint in the cost function of the proposed MPC scheme. The threshold current for toggling action is based on the reference active and reactive power setpoints plus an acceptable band of variation. The theoretical analysis is verified by several case studies.

Power grids with increasing number of distributed energy resources (DERs) equipped with fleet of smart devices are exposed to malicious attacks. These malicious actions can ultimately cause a large-scale blackout if these subversive activities are not prevented, detected, or promptly addressed. Power grids are being threatened by a category of cyber-physical attacks, which target both the physical and cyber layers of the system. This paper proposes an autonomous detection and corrective control framework consisting of two algorithms to identify anomalies and provide a corrective action on the distribution system using smart inverters. The proposed framework detects the inverter abnormal behaviors and identifies them as cyber-physical attack or internal failure of the inverter. A model predictive control (MPC) scheme is proposed to detect the inverter internal failure. In the case of inverter failure, the proposed MPC scheme adopts corrective actions to restore the inverter operation with a pre-defined power injection set-points. Additionally, this paper proposes a cyber-physical attack detection mechanism, based on measurements from a geographic community of smart devices. The proposed framework continuously assists the supervisory control and data acquisition (SCADA) system to differentiate anomalies on the distribution system and decide the appropriate control actions for the entire grid.

Model predictive controlled (MPC) power electronics converters (PECs) features numerous benefits including fast transient response, multi-objective control in single-loop, and inclusion of constraints and nonlinearities in a simple manner. However, the control approach requires the accurate knowledge of the model parameters of the system. Potential model parameter mismatches with the actual values will result in performance deterioration of the MPC. Various agents like aging and harsh environmental conditions would yield changes in circuit impedance. This paper proposes an autonomous MPC for PECs subject to the variations and uncertainties in input filter impedance. The proposed controller adaptively tunes the model parameters used in the predictive equations, alleviating mismatch among the predictive model parameters and the actual system. The proposed auto-tuned modeling scheme requires no additional sensors while maintaining the simplicity of the controller. The proposed method is applicable to all types of PECs and filter topologies. The theoretical analysis and results demonstrate that the proposed approach embedded in the MPC makes the control algorithm robust to variations of model parameters of the system.

This paper proposes a hierarchical optimal active and reactive power control while minimizing the switching events for three-phase grid-tied cascaded multilevel inverters (CMI) for battery energy storage applications. Model predictive control (MPC) is known as a potential approach for multi-objective control schemes in single-loop manner for power electronics interfaces. However, MPC schemes are suffering from high computational burden. Furthermore, one of the main challenges while designing MPC is tuning of the cost function weight factors in multi-objective control schemes. The weight factors design and tuning directly affect the performance and robustness of the MPC. The proposed high performance hierarchical multi-objective optimal controller doesn’t have the aforementioned limitations of the MPC. The proposed control scheme utilizes a dynamic look-up matrix as an internal optimizer tool. The redundant switching states are cycled to equalize the power drawn from the independent battery energy storage sources while achieving a minimum energy control. The theoretical analysis and case studies verify robustness, fast dynamic response, and computational efficiency of the proposed multi-criteria optimal controller.

This work presents a modified model predictive control (MPC) with open-circuit fault-detection and fault-tolerance capabilities for grid-connected multilevel inverters. The proposed self-healing model predictive control (SMPC) approximates the error in the inverter output voltage, and executes a diagnostic algorithm if this error exceeds a pre-defined tolerance. This diagnostic algorithm determines the switch with an open-circuit failure in a duration less than one fundamental period. After an open-circuit failure is detected, the controller will reduce the set of permissible control actions, allowing the inverter to retain healthy operation. Unlike the existing methods, the proposed SMPC combines fault-detection techniques with the direct application for corrective actions for fast fault clearness. The theoretical analysis and case studies verify the functionality of the proposed self-healing MPC scheme for a grid-tied seven-level cascaded multilevel inverter (CMI).

This paper presents a hierarchical finite-set model predictive control (MPC) scheme for grid-tied cascaded multilevel inverters (CMI) with independent active and reactive power injection capabilities. The proposed controller has a hierarchical framework to eliminate the computationally burdensome cost function optimization and associated weight factors of the control objectives. The control formulation approach allows for multi-objective optimization with an error-tolerance framework. The control scheme achieves active and reactive power control with switching loss minimization while extracting power evenly from the independent voltage sources. The control is modularized for each phase, making the system robust to unbalanced grid conditions. The proposed control scheme is validated for grid-voltage transients in MATLAB/Simulink.

This paper presents a decoupled power control scheme for grid-tied single-phase quasi-impedance-source cascaded multilevel inverter (qZS-CMI) for photovoltaic (PV) applications. The proposed control scheme for the grid-tied qZSI-CMI is based on finite-set model predictive control (MPC) to harvest the desired active power from PV modules with ability to provide ancillary services for the grid. The proposed control scheme has two modes of operation: normal grid mode and low voltage ride through (LVRT) mode. In normal grid mode, the controller commands the qZS-CMI to operate at global maximum power point (MPP). The proposed technique regulates the impedance network elements' voltage and current according to the MPP of PV panels and grid voltage/current requirements. In LVRT mode, the controller commands the qZS-CMI to provide the required reactive power for grid during voltage sag as an ancillary service from the inverter as imposed by grid codes. The main features of the proposed system include: global MPP operation during normal grid condition, LVRT capability during grid voltage sag, mitigation of the PV modules mismatch on overall energy harvesting, and seamless transition between normal grid and LVRT modes of operation. Several real-time experiments are conducted to verfiy the system performance with transients in both solar irradiance and the grid voltage.

Short-term load forecasting plays a major role in the operation of electric power systems to ensure instantaneous balance between electricity generation and demand. The accuracy of the forecast generated by a neural network (NN) has several factors, including but not limited to, the algorithm used to train the network, how much and what kind of data are used in the network's training set, how many hidden layers are in the NN, and the size of the hidden layer(s). We investigate the best combination of these factors to decrease the mean absolute percent error (MAPE) and to give the best forecast possible. Based on system load data from the Electric Reliability Council of Texas (ERCOT), this paper focuses on a comprehensive understanding of the accuracy of forecasts generated by NN's with different algorithms, while varying the length of the network's training set, hidden layer size, number of hidden layers, and the addition of data sets when training to create a forecast with the highest accuracy possible.

This paper presents a decoupled active and reactive power predictive control for quasi-Z-source (qZS) single-phase grid connected photovoltaic (PV) microinverter with low voltage ride through (LVRT) capability. The control algorithm structure is based on model predictive control (MPC) framework to facilitate switching between modes of operation: maximum power point tracking (MPPT) and LVRT modes. The proposed controller extracts the maximum power from the PV panels during the normal grid condition. The system is capable of injecting reactive power based on LVRT requirements by the grid codes based on commanded reactive power injection strategy. The proposed algorithm has simple structure with less complexity and computational time for easier and effective implementation. It can respond to rapidly changing PV ambient and grid conditions and appropriately alter the current injection. The performance of the controller is verified experimentally for three reactive power injection strategies.