Dianfei Yang

Bio: Dianfei Yang is an academic researcher from Hohai University. The author has contributed to research in topics: Multi-swarm optimization & Photovoltaic power station. The author has an hindex of 2, co-authored 2 publications receiving 40 citations.

Photovoltaic power station output predicting method based on optimal similar day set

Abstract: The invention discloses a photovoltaic power station output predicting method based on the optimal similar day set. The photovoltaic power station output predicting method includes steps of 1) subjecting historical data to exception handling; 2) normalizing weather types and solar irradiance as characteristic vectors of similar output level days and clustering the same for analysis through cluster algorithm and determining the final similar output level day set; 3) selecting photovoltaic generation output power as characteristic vectors of similar curve-shape days, normalizing the photovoltaic generation output power and then clustering the same for analysis through the cluster algorithm so as to obtain classification results of different cluster numbers, and finally determining a final similar curve-shape day set; 4) acquiring the type which predicting days belong to according to the maximum principle of coefficient of association; 5) constituting the optimal similar set; 6) utilizing the optimal similar set as input and establishing an output predicting model to predict photovoltaic power of the predicting days. The photovoltaic power station output predicting method can accurately predict by selecting a history data with the highest correlation with a predicting day and is simple and feasible, and accuracy in prediction of the photovoltaic generation output power is improved.

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

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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.

Global Maximum Power Point Tracking under Shading Condition and Hotspot Detection Algorithms for Photovoltaic Systems

Abstract: Photovoltaic (PV) technology has been gaining an increasing amount of attention as a renewable energy source. Irradiation and temperature are the two main factors which impact on PV system performance. When partial shading from the surroundings occurs, its incident shadow diminishes the irradiation and reduces the generated power. Moreover, shading affects the pattern of the power–voltage (P–V) characteristic curve to contain more than one power peak, causing difficulties when developing maximum power point tracking. Consequently, shading leads to a hotspot in which spreading the hotspot widely on the PV panel’s surface increases the heat and causes damage to the panel. Since it is not possible to access the circuit inside the PV cells, indirect measurement and fault detection methods are needed to perform them. This paper proposes the global maximum power point tracking method, including the shading detection and tracking algorithm, using the trend of slopes from each section of the curve. The effectiveness was confirmed from the dynamic short-term testing and real weather data. The hotspot-detecting algorithm is also proposed from the analysis of different PV arrays’ configuration, which is approved by the simulation’s result. Each algorithm is presented using the full mathematical equations and flowcharts. Results from the simulation show the accurate tracking result along with the fast-tracking response. The simulation also confirms the success of the proposed hotspot-detection algorithm, confirmed by the graphical and numerical results.

Enhancing the Performance of PV Panel Undergoing Shading Effects

Abstract: Experimental and simulation work were performed to study the effects of shading for different strings inside photovoltaic (PV) panels under real outdoor environmental climate conditions for Sharjah, United Arab Emirates. The electrical characteristics of PV panel were measured by using the PV analyzer, while the simulations were performed by MATLAB. The effect of full cell shading were studied experimentally for each configuration of two or four bypass diodes and verified by theoretical modeling. The I-V and P-V curves for all cases were recorded to investigate the effect of bypass diodes in reducing shading losses under similar environmental conditions. Compared to design with fewer bypass diodes, inserting more bypass diodes (four diodes in our work) contributed to higher yield in performance of solar PV panels undergoing shading while taking in to account the cost of energy production. Compared to the case of two bypass diodes, applying four diodes can recover up to ~31% of non-shaded maximum power under different cell shading conditions.

Experimental study of the effect of fully shading on the Solar PV module performance

Abstract: Experimental tests were performed to study the effects of shading for different string inside the photovoltaic (PV) panels, power equipped with different number of diodes from the same manufacturer as of solar panel. The IV curve for all cases were recorded to see how the bypass diodes will reduce the effects of shading .The case for 3 by pass diode show the best performance of solar PV module under shading phenomena.