Solar energy is one of the most promising renewable energy resource due to its variety of advantages. The photovoltaic systems have a remarkable development over the past few decades. As the maximum power point of the photovoltaic system varies with the change in environmental conditions, the maximum power point tracking technology is necessary to harvest maximum power from the photovoltaic systems. However, multiple peaks occur in the power-voltage (P-V) curve during partial shading conditions. In such condition, many traditional maximum power point tracking methods like perturbation and observation, and incremental conductance may become invalid due to involvement in the local maximum power point. Many advanced methods based on the artificial intelligence like artificial neural network, and fuzzy logic control can track the global maximum power point. However, they are not feasible in real complex environment because they need massive training and broader experience. Alternatively, bio-inspired maximum power point tracking algorithms deal properly with such situations. In recent years, researchers have widely applied bio-inspired algorithms to track the global maximum power point of photovoltaic system during partial shading situations. This paper presents a comprehensive review of the bio-inspired algorithms used for global maximum power point tracking. Various tracking methods are discussed and compared in terms of their characteristics and corresponding improved methods. It also presents the advantages and disadvantages of each method. The modified and combined forms of these methods found to have better performance than original algorithms. Overall, the performance of swarm intelligence based algorithms is found better than evolutionary algorithms. This review may help the researchers to acquire comprehensive information about the application of bio-inspired algorithms to gain maximum power from the photovoltaic systems, and furthermore, help them to choose an efficient way of global maximum power point tracking in photovoltaic systems during partial shading conditions.

Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review

A new fuzzy logic proportional controller approach applied to individual pitch angle for wind turbine load mitigation

Photovoltaic (PV) technology has been the focus of interest due to its nonpolluting operation and good installation flexibility. Irradiation and temperature are the two main factors which impact the performance of the PV system. Accordingly, when partial shading from surroundings occurs, its incident shadow diminishes the irradiation and reduces the generated power. Since the conventional maximum power point tracking methods (MPPT) could not distinguish the global maximum power of the power-voltage (P-V) characteristic curve, a new tracking method needs to be developed. This paper proposes a global maximum power point tracking method using shading detection and the trend of slopes from each section of the curve. Full mathematical equations and algorithms are presented. Simulations based on real weather data were performed both in short-term and long-term studies. Moreover, this paper also presents the experiment using the DC-DC synchronous and interleaved boost converter. Results from the simulation show an accurate tracking result and the system can enhance the total energy generated by 8.55% compared to the conventional scanning method. Moreover, the experiment also confirms the success of the proposed tracking algorithm.

https://www.mdpi.com/1996-1073/12/2/202/pdf

Partial Shading Detection and Global Maximum Power Point Tracking Algorithm for Photovoltaic with the Variation of Irradiation and Temperature

Ultimate loads and response analysis of a monopile supported offshore wind turbine using fully coupled simulation

Abstract Offshore wind farms (OWFs) have received widespread attention for their abundant unexploited wind energy potential and convenient locations conditions. They are rapidly developing towards having large capacity and being located further away from shore. It is thus necessary to explore effective power transmission technologies to connect large OWFs to onshore grids. At present, three types of power transmission technologies have been proposed for large OWF integration. They are: high voltage alternating current (HVAC) transmission, high voltage direct current (HVDC) transmission, and low-frequency alternating current (LFAC) or fractional frequency alternating current transmission. This work undertakes a comprehensive review of grid connection technologies for large OWF integration. Compared with previous reviews, a more exhaustive summary is provided to elaborate HVAC, LFAC, and five HVDC topologies, consisting of line-commutated converter HVDC, voltage source converter HVDC, hybrid-HVDC, diode rectifier-based HVDC, and all DC transmission systems. The fault ride-through technologies of the grid connection schemes are also presented in detail to provide research references and guidelines for researchers. In addition, a comprehensive evaluation of the seven grid connection technologies for large OWFs is proposed based on eight specific indicators. Finally, eight conclusions and six perspectives are outlined for future research in integrating large OWFs. 

/pdf/a-critical-survey-of-technologies-of-large-offshore-wind-1avk3alj.pdf

A critical survey of technologies of large offshore wind farm integration: summary, advances, and perspectives

This paper adopts an adaptive inertial weight particle swarm optimization (AIWPSO) algorithm to improve the maximum power point tracking (MPPT) capability for photovoltaic (PV) system under partial shading condition. Partial shading is a common phenomenon in PV generation system, it causes imbalance and decreases for output power of PV array. Under partial shading condition, output characteristics of PV system will change and the P-V characteristic curve contains more than one peak, which makes the conventional algorithm for MPPT is difficult to track the practical MPP. Particle swarm optimization (PSO) algorithm is often used in MPPT under partial shading condition, but PSO algorithm has the disadvantages of low convergence speed and search accuracy. In this paper, AIWPSO algorithm is proposed to solve these problems. In AIWPSO algorithm, a nonlinear dynamic inertia weight factor is introduced into the PSO evolution to improve global searching ability of PSO algorithm. Simulation results for constant partial shading and rapid changing partial shading show that the proposed algorithm can avoid premature convergence effectively and has good global searching capability.

MPPT of PV system under partial shading condition based on adaptive inertia weight particle swarm optimization algorithm

Summary

This paper presents the vibration reduction strategy of large-size pitch-controlled variable-speed wind turbine based on pitch control and torque control. First, the torque control below the rated operating area and the pitch control above rated operating area are presented, and the control method to reduce the coupling between torque control and pitch control is discussed. Then the vibration mechanism and the modal analysis of the main components (the blade, tower and drive train) of wind turbine are studied. In terms of alleviating the blade flap-wise vibration and drive train torsion vibration, the individual pitch control strategy and torque damping control strategy are proposed to reduce the fluctuations of blade flap-wise load and add damping to the drive train torsion vibration mode. Finally, simulations were conducted using the GH Bladed 1.5-MW wind turbine, and the results show that the proposed vibration reduction strategy can effectively alleviate the blade flap-wise vibration and drive train torsion vibration. Besides, because of the coupling relationship of blade/tower/drive train vibration modes, the proposed strategy can also mitigate the tower fore-aft and side-side vibration. Copyright © 2016 John Wiley & Sons, Ltd.

Vibration reduction strategy for wind turbine based on individual pitch control and torque damping control

Large penetration of intermittent renewable energy and complex loads in Active Distribution Network (ADN) has aggravated the fluctuation of voltage and increased power loss. Battery energy storage system (BESS) is a critical device in ADN, which are used to provide active power for the system. However, by connected with the grid using converter, battery has the ability to provide reactive power for the grid without other reactive compensation devices. To exploit the potential of BESS to regulate voltage fluctuations in ADN, in this paper, an active and reactive power coordinated optimization of BESS is proposed, with the goal of realizing lowest power loss as well as minimal voltage fluctuations in ADN. Then a specific solution to this optimal problem is given based on particle swarm algorithm. The results of a case study show that, by comparison between active power control strategy and active and reactive power coordinated control strategy, this paper has confirmed that the latter with reactive output of battery can have a better effect on reducing the power loss and stabilizing voltage fluctuation.

Active and reactive power coordinated control strategy of battery energy storage system in active distribution network

The invention discloses a photovoltaic power station generation power prediction method comprising the steps that six meteorological characteristics are daily extracted by using the historical meteorological data of a photovoltaic power station so that a meteorological characteristic library is established; the daily characteristic data in the meteorological characteristic library are clustered through a KFCM algorithm so as to realize weather type classification, and class marking is performed on the daily power data and the meteorological data; an SVR sub-model is established for each classof power data and meteorological data according to the class mark; the weather type of the target day is identified by using the SVM through the target day weather characteristics provided by numerical weather prediction and the corresponding SVR sub-model is selected; an ARIMA model is established by using the real-time monitoring data of the target day, and real-time prediction of the irradiation intensity and the temperature can be realized by using the rolling prediction model; and the prediction values of the irradiation intensity and the temperature are inputted to the selected SVR sub-model so that the photovoltaic power station power prediction result can be obtained. The photovoltaic power station generation power prediction accuracy can be enhanced.

Photovoltaic power station generation power prediction method

The integration of wind power with variability brings a series of problems to the dispatching of power grid. To solve this problem, this paper proposes an optimal configuration method of hybrid energy storage tracking wind power planning. First, the fluctuation between planned and actual wind power output, as well as the battery energy storage regulation demand is introduced. Then, the power distribution methods of lithium batteries and super-capacitors in hybrid energy storage are studied. Finally, considering the energy storage operating constraints, the optimal configuration model of hybrid energy storage tracking wind power plan is established. Case study results show that the proposed method can reduce the cost and the deviation between reported and actual output of wind power by hybrid configuration of two types of energy storage batteries.

Haoming Liu

Papers

MPPT of PV system under partial shading condition based on adaptive inertia weight particle swarm optimization algorithm

Vibration reduction strategy for wind turbine based on individual pitch control and torque damping control

Active and reactive power coordinated control strategy of battery energy storage system in active distribution network

Photovoltaic power station generation power prediction method

Optimal Configuration Method of Hybrid Storage System Tracking the Wind Power Plan