Mitigation of power quality problems in PMSG‐based power generation system using quasi‐Newton–based algorithm

Abstract

Summary This paper presents the implementation of quasi‐Newton least mean fourth (QNLMF) control algorithm in the area of wind‐based off‐grid three‐phase four‐wire distributed power generation system for mitigation of power quality problems. Using the proposed control, reference current for voltage source converter is generated in such a way that the power quality problems such as load unbalancing, neutral current compensation, harmonics mitigation, reactive power compensation, and voltage and frequency regulation are observed. More importantly, in the proposed control, the step size parameter, which governs the convergence speed, is independent from the covariance and means of input signal, unlike other stochastic‐based algorithms where the step size parameter is fully dependent on the statistics of the input signal. As a result, the theoretical and practical evaluation shows that the QNLMF control method enjoys an independent performance from the eigenvalue spread of input signal. The proposed control is more suitable for the adopted system because of its fast and accurate dynamic response in uncertain conditions such as wind speed variations. The control is implemented on three‐phase four‐wire system, and performances are found satisfactory under steady state and dynamic state as per IEEE‐519‐2014 standards.

Integration of field-oriented and steady-state linear Kalman filter control in PMSG-based grid-connected system for improving voltage control and power balance operation

“…The step size parameter, which governs system convergence, is unaffected by the input signal's covariance and means in the proposed control. Conversely, algorithms based on stochastic processes rely entirely on the statistical properties of the input signal to determine the step size [9].…”

Adaptive Ɩ0-LMS based algorithm for solution of power quality problems in PV-STATCOM based system

“…References in [8]- [10] used a different variant of LMS algorithm in which the regularisation parameter is updated dynamically and in [11], fast fourier transform (FFT) and fast hartley transform (FHT) is employed for reducing computational complexity. Giri et al [12]- [14] demonstrate variation in the basic LMS algorithm to increase system dynamic response and to adapt to variable input conditions. Research by Agarwal et al [15], the learning loss mitigation funding (LLMF) theory is proposed in which a leakage factor is introduced to alleviate stalling and stability system response.…”

Performance evaluation of different configurations of system with DSTATCOM using proposed Icos⁡ϕ technique

“…Comparisons of phase locked loop (PLL) based control mechanism with DSTATCOM have been implemented for mitigation of load created power quality problems [23]. The quasi newton least mean fourth based control mechanism has been described with DSTATCOM and used mitigation of power quality problems in PSMG which is used as wind generation unit [24]. The optimal step least mean square (LMS) based control technique with DSTATCOM in three phase distribution system has been presented for harmonic suppression and reactive power compensation [25].…”