This article gives a survey of the commonly used methods for harmonic detection in active power filters (APFs). The work proposes a simulation setup that decouples the harmonic detection method from the active filter model and its controllers. In this way, the selected methods can be equally analyzed and compared with respect to their performance, which helps in anticipating possible implementation issues. A comparison is given that may be used to decide the future hardware setup implementation. The comparison shows that the choice of numerical filtering is a key factor for obtaining a good accuracy and dynamic performance of an active power filter.

Detection is key - Harmonic detection methods for active power filter applications

Two major problems that are faced by doubly fed induction generators are: weak low-voltage ride-through capability and fluctuating output power. To solve these problems, a superconducting fault-current limiter-magnetic energy storage system is presented. The superconducting coil (SC) is utilized as the energy storage device for output power smoothing control during normal operation and as a fault-current limiting inductor to limit the surge current in the stator or rotor during the grid fault. The SC can also weaken the rotor back electromotive force voltage, and thus enhance the controllability of the rotor-side converter (RSC), which helps to protect both the RSC and the gearbox. Simulation results verify the efficacy of the proposed approaches.

Enhancing Low-Voltage Ride-Through Capability and Smoothing Output Power of DFIG With a Superconducting Fault-Current Limiter–Magnetic Energy Storage System

In this paper, an optimal unbiased finite impulse response (OUFIR) filter is proposed as a linking solution between the unbiased FIR (UFIR) filter and the Kalman filter (KF). We first derive the batch OUFIR estimator to minimize the mean square error (MSE) subject to the unbiasedness constraint and then find its fast iterative form. It is shown that the OUFIR filter is full horizon (FH) and that its estimate converges to the KF estimate by increasing the horizon length $N$ . As a special feature, we note that the FH OUFIR filter operates almost as fast as the KF. Several other critical properties of the OUFIR filter are illustrated based on simulations and practical applications. Similar to the UFIR filter, and contrary to the KF, the OUFIR filter is highly insensitive to the initial conditions. It has much better robustness than KF against temporary model uncertainties. Finally, the OUFIR filter allows for ignoring system noise, which is typically not well known to the engineer.

Fast Kalman-Like Optimal Unbiased FIR Filtering With Applications

The monitoring and rejection of voltage and current harmonics in power electronics applications such as power quality conditioners or distributed generation systems require correct estimation algorithms especially if the harmonic amplitudes are time varying. Power signal decomposition in multiple synchronous rotating reference frames (MSRFs) is considered one of the best solutions. The most commonly employed implementations of this signal transformation are based on phase-locked loops (PLLs), recursive discrete Fourier transforms (RDFT), or discrete Kalman filtering (DKF). In this paper, a rigorous analysis of the performance of these implementations has been carried out. Complete tests have been performed to evaluate the computational burden, the frequency domain response, and the tolerance to low frequency amplitude variations. The results make it possible to select the proper method depending on the requirements of each application.

A Comparative Analysis of Real-Time Algorithms for Power Signal Decomposition in Multiple Synchronous Reference Frames

This article focuses on problems and issues related to PQ and power system diagnostics, in particular those where signal processing techniques are extremely important. PQ is a general term that describes the quality of voltage and current waveforms. PQ problems include all electric power problems or disturbances in the supply system that prevent end-user equipment from operating properly.

Bridging the gap between signal and power

Knowledge of the process noise covariance matrix Q is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of Q for large time varying systems. This paper looks at an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking. The method models the system as a linear frequency independent state model and does not require an exact knowledge of the noise covariance matrix Q. As an alternative, the proposed adaptive Kalman filter switches between the two basic Q models for steady-state and transient estimation. Its adaptive function allows for the resetting of the Kalman gain to avoid Kalman filter divergence problems under steady-state and allow fast tracking of system variations in transient conditions. Simulation results on the 220 kV network of the lower South Island of New Zealand are presented to validate this approach.

An adaptive Kalman filter for dynamic harmonic state estimation and harmonic injection tracking

Voltage dip is a frequently occurring power-quality problem that electricity consumers face. Knowledge of the source and magnitude of the voltage dip is desirable; however, complete monitoring of the power system is prohibitive. State estimation for both fundamental and harmonic frequencies was presented previously. Its ability to utilize limited measurements for system-wide estimation has proven to be very effective. In this paper, the technique is extended further to estimate power system transients. Transient state estimation (TSE) provides an effective and systematic approach in locating and identifying remote fault sources, thus allowing the problem to be addressed more efficiently. The proposed approach formulates the system using state-space theory and results in a system of first-order differential equations which are used to form the measurement system. The estimation is the simultaneous solution of the measurement system equations based on a time sequence of snapshots of the system measurements. Additional measurement information utilizes the voltage and current history measurements where they are approximated using backward Euler integration formula, are also supplied to the TSE algorithm. To evaluate the performance of the proposed TSE, transients caused by a sudden lost of a system load have been simulated on the test system. This paper provides a new methodology to estimate a complete transient profile of a power system.

An Approximate Method for Transient State Estimation

The effectiveness of harmonic state estimation (HSE) in identifying the location and magnitude of harmonic sources is largely dependent on the accuracy of the measurements. Measurement errors (or bad data) can be classified into two groups; measurement noise and gross error. This paper uses a statistical approach (cumulative probability density functions) obtained from five thousand Monte Carlos runs to investigate the impact of measurement noise and gross errors in harmonic state estimation. The Lower South Island of the New Zealand system is used as the test system and the results are probability curves containing the statistics of the estimation error. The effect of additional measurements on an over-determined system to filter noise is also discussed.

K.K.C. Yu

Papers

An adaptive Kalman filter for dynamic harmonic state estimation and harmonic injection tracking

An Approximate Method for Transient State Estimation

Error analysis in static harmonic State estimation: a statistical approach