This paper presents a novel application of continuous mixed $p$ -norm (CMPN) algorithm-based adaptive control strategy with the purpose of enhancing the low voltage ride through (LVRT) capability of grid-connected photovoltaic (PV) power plants. The PV arrays are connected to the point of common coupling (PCC) through a DC-DC boost converter, a DC-link capacitor, a grid-side inverter, and a three-phase step up transformer. The DC-DC converter is used for a maximum power point tracking operation based on the fractional open circuit voltage method. The grid-side inverter is utilized to control the DC-link voltage and terminal voltage at the PCC through a vector control scheme. The CMPN algorithm-based adaptive proportional-integral (PI) controller is used to control the power electronic circuits due to its very fast convergence. The proposed algorithm updates the PI controller gains online without the need to fine tune or optimize. For realistic responses, the PV power plant is connected to the IEEE 39-bus New England test system. The effectiveness of the proposed control strategy is compared with that obtained using Taguchi approach-based an optimal PI controller taking into account subjecting the system to symmetrical, unsymmetrical faults, and unsuccessful reclosing of circuit breakers due to the existence of permanent fault. The validity of adaptive control strategy is extensively verified by the simulation results, which are carried out using PSCAD/EMTDC software. With the proposed adaptive-controlled PV power plants, the LVRT capability of such system can be improved.

An Adaptive Control Strategy for Low Voltage Ride Through Capability Enhancement of Grid-Connected Photovoltaic Power Plants

Wind power installations are rapidly increasing worldwide, leading to a huge level of permeation into electricity supply networks. Enormous efforts are spent to improve the performance of the wind turbine generator systems. This paper proposes a novel adaptive fuzzy logic control strategy for performance improvement of a grid-tied wind generator system. The variable-speed wind turbine driven permanent-magnet synchronous generator is tied to the electricity network by a full-capacity power converter. A cascaded adaptive fuzzy logic control strategy is proposed as the control methodology for the generator- and the grid-side converter/inverter. The adaptive technique depends on a continuous mixed $p$ -norm algorithm, which on-line updates the scaling factors of the fuzzy logic controllers (FLCs) at a high convergence speed. For the sake of preciseness, real wind speed data measured in the Zaafarana wind farm, Egypt, are considered in the analyses. The effectiveness of the proposed adaptive FLC is compared to that achieved using particle swarm optimization algorithm based an optimal proportional-integral controller, considering severe grid disturbances. Extensive simulation analyses, which are done using MATLAB/Simulink software, are presented to validate the efficiency of the adaptive fuzzy logic control strategy.

An Adaptive Fuzzy Logic Control Strategy for Performance Enhancement of a Grid-Connected PMSG-Based Wind Turbine

The normalized subband adaptive filter (NSAF) presented by Lee and Gan can obtain faster convergence rate than the normalized least-mean-square (NLMS) algorithm with colored input signals. However, similar to other fixed step-size adaptive filtering algorithms, the NSAF requires a tradeoff between fast convergence rate and low misadjustment. Recently, a set-membership NSAF (SM-NSAF) has been developed to address this problem. Nevertheless, in order to determine the error bound of the SM-NSAF, the power of the system noise should be known. In this paper, we propose a variable step-size matrix NSAF (VSSM-NSAF) from another point of view, i.e., recovering the powers of the subband system noises from those of the subband error signals of the adaptive filter, to further improve the performance of the NSAF. The VSSM-NSAF uses an effective system noise power estimate method, which can also be applied to the under-modeling scenario, and therefore need not know the powers of the subband system noises in advance. Besides, the steady-state mean-square behavior of the proposed algorithm is analyzed, which theoretically proves that the VSSM-NSAF can obtain a low misadjustment. Simulation results show good performance of the new algorithm as compared to other members of the NSAF family.

A Variable Step-Size Matrix Normalized Subband Adaptive Filter

Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to more recent sensor network applications in surveillance, target localization, and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], [5].

Combinations of Adaptive Filters: Performance and convergence properties

This paper presents a novel adaptive control scheme of the superconducting magnetic energy storage (SMES) units with the purpose of smoothing the wind farms' output power. The adaptive control scheme is based on the set-membership affine projection algorithm (SMAPA), which provides a faster convergence and less computational complexity than the normalized least-mean-square algorithm. In this study, two grid-connected fixed-speed wind farms are considered. The control strategy of the SMES units is based on a pulse width modulation (PWM) voltage source converter (VSC) and a dc-dc converter. The VSC and dc-dc converter is used to control the reactive and active power exchange with the power system, respectively. The SMAPA-based adaptive proportional-integral (PI) controllers are utilized to control both converters. For realistic responses, real wind speed data extracted from Hokkaido Island, Japan, and two-mass drive train model of the wind turbine generator system are used in the analyses. Also, a real 10 MVA SMES unit that was installed at a power plant in Kameyama, Japan, is connected to the point of common coupling of the wind farms. The validity of the proposed control scheme is verified by the simulation results, which are performed using PSCAD/EMTDC environment. With the SMAPA-based adaptive-controlled SMES units, the wind farms' output power can be smoothed easily avoiding huge effort for fine tuning the controllers' parameters.

A Set-Membership Affine Projection Algorithm-Based Adaptive-Controlled SMES Units for Wind Farms Output Power Smoothing

Diffusion sign-error LMS algorithm

A sign subband adaptive filter (SSAF) for acoustic echo cancellation is proposed, which is derived by minimising the L1-norm of the subband a posteriori error vector subject to a constraint on the tap-weight vector of the filter. In addition, a variable regularisation parameter (VRP) is introduced to improve the performance of the SSAF. Simulation results are presented to verify the good robustness of the proposed VRP-SSAF against double-talk.

Variable regularisation parameter sign subband adaptive filter

Fast communication: Two variants of the sign subband adaptive filter with improved convergence rate

In hands-free telephones and teleconferencing systems, acoustic echo cancellers are required, which are often implemented by adaptive filters. In these applications, the speech input signal of the adaptive filter is highly correlated and the impulse response of the echo path is very long. These characteristics will slow down the convergence rate of the adaptive filter if the well-known normalized least-mean-square (NLMS) algorithm is used. The normalized subband adaptive filter (NSAF) offers a good solution to this problem because of its decorrelating property. However, similar to the NLMS-based adaptive filter, the NSAF requires a tradeoff between fast convergence rate and small steady-state mean-square error (MSE). In this paper, we propose an adaptive combination scheme to address this tradeoff. The combination is carried out in subband domain and the mixing parameter that controls the combination is adapted by means of a stochastic gradient algorithm which employs the sum of squared subband errors as the cost function. The performance of the proposed combination scheme is evaluated in the context of acoustic echo cancellation (AEC). Experimental results show that the combination scheme can obtain both fast convergence rate and small steady-state MSE.

Jingen Ni

Papers

A Variable Step-Size Matrix Normalized Subband Adaptive Filter

Diffusion sign-error LMS algorithm

Variable regularisation parameter sign subband adaptive filter

Fast communication: Two variants of the sign subband adaptive filter with improved convergence rate

Adaptive combination of subband adaptive filters for acoustic echo cancellation