This paper presents a comprehensive review on the unified power quality conditioner (UPQC) to enhance the electric power quality at distribution levels. This is intended to present a broad overview on the different possible UPQC system configurations for single-phase (two-wire) and three-phase (three-wire and four-wire) networks, different compensation approaches, and recent developments in the field. It is noticed that several researchers have used different names for the UPQC based on the unique function, task, application, or topology under consideration. Therefore, an acronymic list is developed and presented to highlight the distinguishing feature offered by a particular UPQC. In all 12 acronyms are listed, namely, UPQC-D, UPQC-DG, UPQC-I, UPQC-L, UPQC-MC, UPQC-MD, UPQC-ML, UPQC-P, UPQC-Q, UPQC-R, UPQC-S, and UPQC-VA. More than 150 papers on the topic are rigorously studied and meticulously classified to form these acronyms and are discussed in the paper.

Enhancing Electric Power Quality Using UPQC: A Comprehensive Overview

This paper attempts to improve the dynamic performance of a shunt-type active power filter. The predictive and adaptive properties of artificial neural networks (ANNs) are used for fast estimation of the compensating current. The dynamics of the dc-link voltage is utilized in a predictive controller to generate the first estimate followed by convergence of the algorithm by an adaptive ANN (adaline) based network. Weights in adaline are tuned to minimize the total harmonic distortion of the source current. Extensive simulations and experimentations confirm the validity of the proposed scheme for all kinds of load (balanced and unbalanced) for a three-phase three-wire system.

A Shunt Active Power Filter With Enhanced Performance Using ANN-Based Predictive and Adaptive Controllers

This paper proposes a fault detection and classification method for medium voltage DC (MVDC) shipboard power systems (SPSs) by integrating wavelet transform (WT) multiresolution analysis (MRA) technique with artificial neural networks (ANNs). The MVDC system under consideration for future all-electric ships presents a range of new challenges, in particular the fault detection and classification issues addressed in this paper. The WT-MRA and Parseval's theorem are employed in this paper to extract the features of different faults. The energy variation of the fault signals at different resolution levels are chosen as the feature vectors. As a result of analysis and comparisons, the Daubechies 10 (db10) wavelet and scale 9 are the chosen wavelet function and decomposition level. Then, ANN is adopted to automatically classify the fault types according to the extracted features. Different fault types, such as short circuit faults on both dc bus and ac side, as well as ground fault, are analyzed and tested to verify the effectiveness of the proposed method. These faults are simulated in real time with a digital simulator and the data are then initially analyzed with MATLAB. The case study is a notional MVDC SPS model, and promising classification accuracy can be obtained according to simulation results. Finally, the proposed fault detection algorithm is implemented and tested on a real-time platform, which enables it for future practical use.

Fault Detection and Classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks

Stochastically fluctuating wind power has a negative impact on power grid operations. This paper presents a wind power filtering approach to mitigate short- and long-term fluctuations using a hybrid energy storage system (HESS), and a novel wavelet-based capacity configuration algorithm to properly size the HESS. A frequency distribution allocates wind power fluctuations to the different HESS components to more easily satisfy 1-min and 30-min fluctuation mitigation requirements (FMR). An ultra-capacitor bank (UC) mitigates short-term fluctuations. In the HESS, and a lithium-ion battery bank (LB) minimizes long-term fluctuations. This paper also proposes a novel online-wavelet based coordination control scheme for the HESS, consisting of primary filtering (PF) and secondary filtering (SF) stages. The PF stage obtains a combined power output that fully satisfies the FMRs, while the SF stage provides additional smoothing of the wind power output fluctuations after the PF stage. A remaining energy level (REL) feedback control maintains the REL of the battery bank within its proper range. Case studies demonstrate that the proposed wavelet-based algorithm is more efficient than other published algorithms, and needs a lower energy storage capacity to satisfy 1-min and 30-min FMRs.

Wavelet-Based Capacity Configuration and Coordinated Control of Hybrid Energy Storage System for Smoothing Out Wind Power Fluctuations

This paper proposes a wavelet-based data compression approach for the smart grid (SG). In particular, wavelet transform (WT)-based multiresolution analysis (MRA), as well as its properties, are studied for its data compression and denoising capabilities for power system signals in SG. Selection of the Order 2 Daubechies wavelet and scale 5 as the best wavelet function and the optimal decomposition scale, respectively, for disturbance signals is demonstrated according to the criterion of the maximum wavelet energy of wavelet coefficients (WCs). To justify the proposed method, phasor data are simulated under disturbance circumstances in the IEEE New England 39-bus system. The results indicate that WT-based MRA can not only compress disturbance signals but also depress the sinusoidal and white noise contained in the signals.

A Wavelet-Based Data Compression Technique for Smart Grid

In this paper, a new algorithm is proposed to estimate amplitude and phase angle (and frequency) of load currents and source voltages in the presence of harmonics and frequency oscillation. Moreover, a comprehensive control strategy is introduced to extract the compensating signals for the control of series and shunt converters of the unified power-quality conditioner (UPQC). The basic approach used in this algorithm is to extract the fundamental component of load current and source voltage by using wavelet-transform decomposition. Then, the amplitude, phase angle, and frequency of fundamental component are calculated using the well-known LS algorithm, which is relatively simple and fast. The voltage and current references of UPQC active power filters are obtained using the positive sequence of three-phase system voltages and load currents. The proposed control strategy can extract the reference currents and voltages of UPQC fast and accurately in the presence of harmonics and/or frequency oscillation. Simulation results have been presented to show improvement of the UPQC performance in the presence of frequency and amplitude variation condition and various forms of current and voltage distortion

M. Forghani

Papers

Online Wavelet Transform-Based Control Strategy for UPQC Control System