There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Wind power is the most reliable and developed renewable energy source over past decades. With the rapid penetration of the wind generators in the power system grid, it is very essential to utilize the maximum available power from the wind and to operate the wind turbine (WT) at its maximal energy conversion output. For this, the wind energy conversion system (WECS) has to track or operate at the maximum power point (MPP). A decent variety of publication report on various maximum power point tracking (MPPT) algorithms for a WECS. However, making a choice on an exact MPPT algorithm for a particular case require sufficient proficiency because each algorithm has its own merits and demerits. For this reason, an appropriate review of those algorithms is essential. However, only a few attempts have been made in this concern. In this paper, different available MPPT algorithms are described for extracting maximum power which are classified according to the power measurement i.e. direct or indirect power controller. Merits, demerits and comprehensive comparison of the different MPPT algorithms also highlighted in the terms of complexity, wind speed requirement, prior training, speed responses, etc. and also the ability to acquire the maximal energy output. This paper serves as a proper reference for future MPPT users in selecting appropriate MPPT algorithm for their requirement.

A review of conventional and advanced MPPT algorithms for wind energy systems

This article gives an overview of the artificial intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics. This article is accompanied by an Excel file listing the relevant publications for statistical analytics.

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An Overview of Artificial Intelligence Applications for Power Electronics

This paper proposes a low-voltage ride-through technique of a doubly fed induction generator (DFIG) wind turbine system using a dynamic voltage restorer (DVR). For effective control of the DVR, digital all-pass filters are used for extracting the positive-sequence component from the unbalanced grid voltage since they have the advantages of giving a desired phase shift and no magnitude reduction over conventional low- or high-pass filters. Using the positive-sequence component, the phase angles for the positive- and negative-sequence components of the grid voltage are derived. A control algorithm for the DVR that is dual voltage controllers only is implemented for the two sequence components in the dq synchronous reference frame. In order to achieve the power rating reduction of the DVR, the stator power reference for the DFIG is reduced during faults. In addition, a control scheme of pitch angle system is applied to stabilize the operation of the wind turbine system in the event of grid faults. PSCAD/EMTDC simulations show the effectiveness of the proposed technique and a feasibility of reducing the power rating of DVR for the fault ride-through capability of DFIG. The validity of the proposed control scheme for the DVR has also been verified by experimental results.

A Fault Ride-Through Technique of DFIG Wind Turbine Systems Using Dynamic Voltage Restorers

Energy storage systems (ESSs) are often proposed to support the frequency control in microgrid systems. Due to the intermittency of the renewable generation and constantly changing load demand, the charging/discharging of various ESSs in an autonomous microgrid needs to be properly coordinated to ensure the supply-demand balance. Recent research has discovered that the charging/discharging efficiency of ESSs has remarkable dependence on the charging/discharging rate and state-of-charge of the ESS. This paper proposes a distributed cooperative control strategy for coordinating the ESSs to maintain the supply-demand balance and minimize the total power loss associated with charging/discharging inefficiency. The effectiveness of the proposed approach is validated by simulation results.

Cooperative Control of Distributed Energy Storage Systems in a Microgrid

In this paper, a novel algorithm for wind-speed estimation in wind-power generation systems is proposed, which is based on the theory of support-vector regression (SVR). The inputs of the SVR wind-speed estimator are chosen as the wind-turbine power and rotational speed. During the offline training, a specified model, which relates the inputs to the output, is obtained. Then, the wind speed is determined online from the instantaneous inputs. The experimental results have verified the validity of the proposed estimation algorithm.

MPPT Control of Wind Generation Systems Based on Estimated Wind Speed Using SVR

This paper proposes a variable speed control scheme of grid-connected wind power generation system using cage-type induction generators, which is based on a fuzzy logic control. The induction generator is operated in indirect vector control mode, where the d-axis current controls the excitation level and the q-axis current controls the generator torque, by which the speed of the induction generator is controlled according to the variation of the wind speed in order to produce the maximum output power. The generated power flows into the utility grid through the back-to-back PWM converter. The grid-side converter controls the DC link voltage and the line-side power factor by the q-axis and the d-axis current control, respectively. Experimental results are shown to verify the validity of the proposed scheme.

Variable speed wind power generation system based on fuzzy logic control for maximum output power tracking

In this paper, a new online capacitance estimation method for dc-link capacitors in a three-phase ac/dc/ac pulsewidth-modulation converter is proposed. A controlled ac voltage with a lower frequency than the line frequency is injected into the dc-link voltage, which then causes ac power ripples at the dc output side. By extracting the ac power component on the dc output side using digital filters, the capacitance can then be calculated using a support vector regression method. A function that defines the relation between a given capacitor power and its corresponding capacitance is determined using a set of training data. This function is then used to predict the output for the given input which is not included in the training set. The proposed method can be simply implemented with only software and no additional hardware. Experimental results confirm that the estimation error is less than 0.15%.

DC-Link Capacitance Estimation in AC/DC/AC PWM Converters Using Voltage Injection

Power versus voltage curves of partial shading photovoltaic (PV) systems contain several local peaks (LPs) and one global peak (GP). Most conventional maximum power point tracker (MPPT) techniques may not follow the GP under partial shading conditions (PSC). The use of metaheuristic techniques such as the bat algorithm (BA) and particle swarm optimization (PSO) can overcome these obstacles. All problems inherent in the using of BA as MPPT of PV systems has been discussed and solved in this paper. The first problem is the random initial values of bats that may cause premature convergence. Therefore, the initial values of bats were modified to be close to the anticipated positions of peaks to reduce the convergence time and improve the chance of capturing the GP. The second problem occurs when shading pattern changes the value and position of the GP which is not configurable because all bats are concentrated at the previous GP; this can be resolved by BA re-initialization. The the third problem is the GP memorized in the execution of the BA code forces the PV system to work at the duty ratio of the highest GP ever seen, which may not be the real GP. This problem is solved by updating the memorized GP. This paper also proposes a new criterion for selecting the optimal swarm size against number of peaks to reduce the convergence time and improve the chance of capturing the GP. To the authors' knowledge, most of these problems inherent in the BA have hitherto not been addressed in the literature. The simulation and experimental results obtained from the proposed modified BA (MBA) with re-initialization have been compared to the PSO and grey wolf optimization (GWO) techniques which show the superiority of using MBA strategy in the MPPT of partial shading PV systems.

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Ahmed G. Abo-Khalil

Papers

MPPT Control of Wind Generation Systems Based on Estimated Wind Speed Using SVR

Variable speed wind power generation system based on fuzzy logic control for maximum output power tracking

DC-Link Capacitance Estimation in AC/DC/AC PWM Converters Using Voltage Injection

A Novel Bat Algorithm Strategy for Maximum Power Point Tracker of Photovoltaic Energy Systems Under Dynamic Partial Shading

Synchronization of DFIG output voltage to utility grid in wind power system