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This paper presents a new method to measure the voltage across the submodule (SM) capacitors in a modular multilevel converter (MMC). The proposed technique requires only one voltage sensor per arm. This reduces the number of sensors required compared to conventional sensor-based methods. Therefore, the cost and complexity of the system are reduced, which in turn improves the converter’s overall reliability. The proposed method employs an exponentially weighted recursive least square (ERLS) algorithm to estimate the SM capacitor voltages through the measured total arm voltage and the switching patterns of each SM. There is thus no need for extra sensors to measure these control signals as they are directly provided from the controller. The robustness of the proposed method is confirmed via introducing deviations for the capacitance values, dynamic load changes, DC voltage change and start-up transient condition. Simulation and experimentally validated results based on a single-phase MMC show the effectiveness of the proposed method in both, steady-state and dynamic operations.

This paper presents a new voltage estimation method for the submodule (SM) capacitor in a modular multilevel converter (MMC). The proposed method employs a Kalman filter (KF) algorithm to estimate the SM voltages of the converter. Compared with sensor-based methods, this scheme requires only one voltage sensor to achieve the voltage-balancing of the converter. This sensor is connected to the total arm voltage; the proposed algorithm requires also the switching patterns of each upper SM switch which are provided by the controller used without the need for extra sensors. The substantial reduction in the number of voltage sensors improves the system reliability and decreases its cost and complexity. Extensive simulation and experimental analyses carried out to validate the proposed estimation scheme under different conditions include steady-state analyses, the effect of variations in capacitance and inductance, of the impact of low carrier and effective switching frequency on the accuracy of the estimation, step changes to the load, and a range changes in DC voltage. The results obtained are experimentally verified using a single-phase MMC.

To achieve high-performance control of modern dc-dc converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. In this paper, a new parametric system identification method, based on a Kalman filter (KF) approach is introduced to estimate the discrete model of a synchronous dc-dc buck converter. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. The proposed KF estimation technique is experimentally verified using a Texas Instruments TMS320F28335 microcontroller platform and synchronous step-down dc-dc converter. Results demonstrate a robust and reliable real-time estimator. The proposed method can accurately identify the discrete coefficients of the dc-dc converter. This paper also validates the performance of the identification algorithm with time-varying parameters, such as an abrupt load change. The proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Furthermore, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical exponentially weighted recursive least-squares method.

In this paper, an improved rotor flux estimation method for the Torque model reference adaptive schemes (TMRAS) sensorless induction machine drive is proposed to enhance its performance in low and zero speed conditions. The conventional TMRAS scheme uses an open loop flux estimator and a feedforward term, with basic low pass filters replacing the pure integrators. However, the performance of this estimation technique has drawbacks at very low speeds with incorrect flux estimation significantly affecting this inherently sensorless scheme. The performance of the proposed scheme is verified by both simulated and experimental testing for an indirect vector controlled 7.5-kW induction machine. Results show the effectiveness of the proposed estimator in the low- and zero-speed regions with improved robustness against motor parameter variation compared to the conventional method.

Synchronous machines are the most widely used form of generators in electrical power systems. Identifying the parameters of these generators in a non-invasive way is very challenging because of the inherent non-linearity of power station performance. This study proposes a parameter identification method using a stochastic optimisation algorithm that is capable of identifying generator, exciter and turbine parameters using actual network data. An eighth order generator/turbine model is used in conjunction with the measured data to develop the objective function for optimisation. The effectiveness of the proposed method for the identification of turbo-generator parameters is demonstrated using data from a recorded network transient on a 178 MVA steam turbine generator connected to the UK's national grid.