An improved model predictive direct power control (MPDPC) for three-phase grid-connected converters is proposed. In the proposed method, two voltage vectors are applied during a control period and their duty cycles are determined by a fuzzy logic-based modulator. The inputs to the modulator are the active and reactive power errors and the output is the duty cycle of the first (main) voltage vector. The fuzzy rules are developed based on expert knowledge and the fact that small/large power errors can be compensated by applying the main voltage vector for a small/large portion of the switching period. The candidate voltage vector pairs are examined on a control Lyapunov function and the pair that satisfy the closed-loop stability criteria are selected. The voltage vector pairs are then applied following a proposed switching pattern through which reduced average switching frequency is achieved. Comparative simulation and hardware-in-the-loop studies between the proposed method and a most-recently introduced duty cycle based MPDPC confirm that in addition to lower average switching frequency, better quality currents and active and reactive powers can be achieved under the proposed MPDPC.

This study presents a simple and efficient approach for grid voltage estimation based on the converter equations in the stationary reference frame. The proposed technique is adopted for the sensorless operation of the voltage source converter‐based power conditioners. In this study, the effectiveness of the proposed estimation method when it is combined with a proportional‐resonant (PR)‐based current controller is investigated. When the PR controller is combined with the proposed voltage estimation technique, it offers an additional benefit, such that the grid voltage synchronisation is done without using any phase‐locked loop or band‐pass filter. The proposed sensorless control method shows an excellent steady‐state and transient performance. It is proven through extensive simulations and experiments that the grid voltage estimation with a very low computational burden is achieved even when the grid voltage is distorted.

The three-phase inductor and capacitor filter (LC)-filtered voltage source inverter (VSI) is subjected to uncertain and time-variant parameters and disturbances, e.g., due to aging, thermal effects, and load changes. These uncertainties and disturbances have a considerable impact on the performance of a VSI’s control system. It can degrade system performance or even cause system instability. Therefore, considering the effects of all system uncertainties and disturbances in the control system design is necessary. In this respect and to tackle this issue, this paper proposes an adaptive model predictive control (MPC), which consists of three main parts: an MPC, an augmented state-space model, and an adaptive observer. The augmented state-space model considers all system uncertainties and disturbances and lumps them into two disturbance inputs. The proposed adaptive observer determines the lumped disturbance functions, enabling the control system to keep the nominal system performance under different load conditions and parameters uncertainty. Moreover, it provides load-current-sensorless operation of MPC, which reduces the size and cost, and simultaneously improves the system reliability. Finally, MPC selects the proper converter voltage vector that minimizes the tracking errors based on the augmented model and outputs of the adaptive observer. Simulations and experiments on a 5 kW VSI examine the performance of the proposed adaptive MPC under different load conditions and parameter uncertainties and compare them with the conventional MPC.

This study presents a proportional resonant current control scheme for three‐phase grid connected voltage source converters without any grid voltage sensors. Towards this goal, the grid voltage sensors are replaced by a voltage estimation scheme based on the Newton–Raphson algorithm. In the proposed method, the grid connected converter behaviour is converted to an optimisation problem which relates the instantaneous grid voltage value to the filter parameters, the generated converter voltage and the exchanged powers in each sampling period. The proposed optimisation problem is then solved in each sampling period by means of the Newton–Raphson optimisation technique. Afterwards, the reference current for the current control loop is generated from the estimated grid voltages. Using an estimator instead of the voltage sensors not only reduces the size and cost of the converter system but also improves its reliability and protects it from grid voltage disturbances and noises. The excellent performance of the proposed grid voltage sensorless scheme is confirmed through experimental and simulation results.

this paper presents an improved deadbeat direct power control (DPC) for grid connected voltage source converters (VSCs). The proposed deadbeat-DPC combines a deadbeat control law with a full order Luenberger observer to predict the converter behavior. The deadbeat control law is chosen to cancel out the power error and the Luenberger observer is adopted to compensate for the control delay and simultaneously to eliminate the grid voltage sensors. Finally, the excellent steady-state and dynamic performance of the proposed method is confirmed through extensive simulations.

This paper proposed a probabilistic framework that could be used for the sensitivity assessment of grid-connected voltage source converters (VSCs), where uncertainties in the grid short circuit ratio (SCR) and operating point conditions, as well as control-loop interactions, were considered. The proposed method tried to broaden the available knowledge on the small-signal stability analysis of VSCs and provide a probabilistic point of view of this subject. It considered the probability of different operational conditions in order to obtain less conservatism and more accurate results. Based on uncertain inputs and the employed stability model, the proposed model produced statistical distributions of the critical mode and its damping factor and ratio, which were not accessible by existing deterministic methods. Crucial statistical information measures how much system stability and performance are maintained or changed over the system uncertainties and disturbances, as well as provides a clear insight into the system stability problem. For instance, as concluded in this paper, for the conventional control system design, fast dynamic parts of a VSC, such as the current controller and control delay, significantly impact the minimum damping ratio. Furthermore, slow dynamic parts, such as outer voltage control loops and the synchronization block, influence the maximum damping factor. For strong grids, the AC voltage magnitude controller (AVC) significantly impacts the maximum damping factor due to its lower bandwidth among all control loops. For weak grids, the damping factor of the critical mode is highly affected by interactions between the VSC, the power grid, and different control loops due to the synchronization mechanism. The other contributions of this paper were the introduction of robust stability and performance definitions and indices; explanations of the pros and cons of probabilistic assessment methods and their applicability; interpretation of the obtained results; and, finally, a link was provided between system stability and reliability, which will be crucial for future power system design.