A Combined MPPT Algorithm for Photovoltaic Systems Based Arduino Microcontroller

In order to improve the trajectory control effect of multi‐degree‐of‐freedom industrial robots, this paper combines visual image technology to conduct research on trajectory control of multi‐degree‐of‐freedom industrial robots. Aiming at the problem of video segmentation under sudden illumination changes, this paper uses a Gaussian mixture model based on the global illumination function to adopt a variety of illumination invariant features, and proposes a scene segmentation algorithm suitable for sudden illumination changes. Moreover, this paper compares and verifies the algorithm from the subjective and objective perspectives through experiments, which shows that the algorithm in this paper can segment the scene more accurately even in the environment of sudden changes in illumination. In addition, the results of the accuracy test and the trajectory control test show that the research method of the multi‐degree‐of‐freedom industrial robot trajectory control based on the visual image proposed in this paper can effectively improve the trajectory control effect of the robot.

Dynamic backgrounds and sudden illumination changes are two of the major problems associated with background subtraction techniques. In this paper, we present a novel approach to background subtraction that addresses both of these challenges. In particular, we present an improved codebook background modelling and subtraction technique. We utilise image segmentation on the background image and model the background with a codebook for each pixel along with a pseudo background layer. We perceive background motion as an occlusion of one background layer by a nearby background layer. In other words, sliding of one background layer over a neighbouring layer causes background motion and will hence result in false segmentation. We present our approach of codeword spreading across layer boundaries to handle background motion. Furthermore, we present a two-step update of the background codebook to handle both sudden and gradual illumination changes.

The schooling behavior of both light- and dark-adapted Astyanax mexicanus (Fillipi) was observed under sudden changes in illumination in the range 10−7 to 100 ft-c at different times in the diel cycle. In darkness the fish were dispersed, swam slowly, and showed no signs of mutual recognition of one another. There was no simple relationship between the schooling response and the illumination, but in general the frequency, size, and durability of schools were proportional to the illumination. Schooling was maximal above 10−1 ft-c and approached zero at 10−6 ft-c. The schooling at any level of illumination varied with the testing procedures. The lowest responses were obtained with a gradual reduction of illumination in the evening. At illumination below 10−2 ft-c maximal schooling was obtained from dark-adapted fish. From 10−2 ft-c upward, it was obtained from light-adapted fish. The schooling rate (number of fish in a school) reached 100% for a few seconds at 10−3 ft-c and for longer periods at higher illumination, but was never sustained at the 100% level throughout any 4-min period. Schooling was generally enhanced by any sudden change in illumination but was never sustained at a high rate under monotonous conditions. The decline with monotony was equated with reduction of the fright situation. The decline with reduced illumination was attributed to the reduction of visual acuity and visibility, and occurred at a greater rate among faster-swimming fish. Notemigonus crysoleucas (Mitchill), swimming very slowly, exhibited high schooling rates at 10−5 ft-c. Earlier work showed that the retina of Astyanax in continuous darkness undergoes a rhythmic diel shift. The behavior showed that this retinal shift is not the classical transformation from scotopic to photopic vision. Dark-adapted during the day, the fish demonstrated rapid visual perception under sudden illumination at any value from 10−6 to 10−0.69 ft-c. Dark-adapted fish at night exhibited increasingly longer light-shock reactions with sudden illumination above 10−2 ft-c. According to schooling responses the cone threshold was about 10−3 ft-c, and the rod threshold about 10−6 ft-c. The mesopic range extended from about 10−3 to 10−2 ft-c.

Developmental changes in behavioral and retinomotor responses of Pacific bluefin tuna on exposure to sudden changes in illumination

A performance evaluation model of a high concentration photovoltaic module with a fractional open circuit voltage-based maximum power point tracking algorithm

The expansion of the spread of solar photovoltaic power generation systems is fraught with challenges related to the low efficiency and performance of these systems in light of the change in the surrounding weather conditions and the disturbances associated with the loads, which weakens the electrical energy productivity of these photovoltaic systems due to energy loss. To overcome and reduce these challenges as much as possible, it is considered the use of controllers that track the maximum power point of photovoltaic systems is the ideal solution to increase their efficiency and improve the quality of their electrical power, in order to obtain the maximum productive capacity from these systems, and this will be reflected in achieving an optimum benefit from the available solar energy and thus reducing the loss of unused energy in the absence of the control system.This paper presents a study, analysis and simulation of the actual behaviour of the grid-connected solar photovoltaic system model by means of the (MATLAB/Simulink_R2018a) program. The performance of Fuzzy Logic Controller is then evaluated to track the path of the maximum power point, and its work is based on tracking techniques that use the direct control method to control the operating cycle of the DC commutator, which in turn performs by feeding the inverter with the maximum output power from the solar photovoltaic system.A tracking technology was selected that relies on the method of artificial intelligence, which is the technique (fuzzy logic controller), and a fuzzy logic controller of the type (Mamdani) was used. The most suitable for tracking the maximum power point at the steady state of the Grid and under constant and changing weather conditions, as well as choosing the most appropriate controller when transient disturbances occur in the Grid under constant weather conditions only. The results proved the effectiveness of the fuzzy logic controller, and it had good performance in tracking the maximum power point in terms of achieving more stability, reducing error and settling time.

In renewable system control engineering, Maximum Power Point Tracking (MPPT) techniques are considered as one of the key steps in photovoltaic (PV) system development. Indeed, some MPPT techniques provide high performance under uniform irradiation and temperature conditions. However, their performance significantly degrades when the two preceding climatic conditions suddenly change. Therefore, this paper proposes a new voltage regulation based on Fuzzy Logic Control (FLC) strategy and small-signal model of DC–DC booster converter. The proposed control scheme is mainly based on the indirect incremental conductance (Inc) technique, equipped with an optimal integrator controller (OIC). It is implemented in a PV system, consisting of a DC–DC boost converter connected to a resistive load, with the main objective of ensuring a good MPP tracking behavior for variable operating conditions. To achieve this objective, the proposed MPPT strategy is designed around two stages. First, the smooth reference voltage is ensured by a Single-Input Single-Output (SISO) fuzzy logic controller using the Inc technique based on the MPPT approach. Then, the optimal integrator controller stabilizing the closed-loop system is integrated with optimization of its parameter according to Routh’s criterion for resistive load variations. Simulation results confirm the effectiveness of the proposed MPPT technique compared to some commonly used MPPT approaches. The superiority of the proposal is proven by using efficiency ratio, which rises more than 98% in all case studies. These objectives are ensured for both uniform and critical sudden change in weather conditions, regardless of resistive load variations.

This paper presents a moving object detection method against sudden illumination change using improved background modeling. Initially, background model is created for every pixel from the first frame. The sample values for the model of a pixel are collected from the neighborhood of that pixel. Then the new pixel values from the new frames are compared to make background foreground decision. Conventional background modeling faces problem with change in the illumination of the scene. The proposed method frequently checks whether abrupt change of illumination take place or not and then initialize the background model from the frame that is detected with changed illumination. The illumination change is detected by obtaining the images of two frames that are taken at a suitable interval in HSV color space. Then the mean change value of each channel is calculated to make a decision. This enables the background model to start over with new sample values that are obtained in the current illumination condition and the background subtraction process can successfully detect moving object with greater accuracy even in changing illumination condition. Simulation results indicate that the proposed method gives excellent results in illumination changing condition to detect moving object whereas the conventional background modeling can not detect accurately. Comparison analysis shows that our proposed method outperforms recent methods in terms of detection accuracy.

본 논문은 조명의 변화가 심한 영상에서 손 형태를 안정적으로 인지하는 기법에 관한 것이다. 제안한 방법은 HSI 색상공간에서 색상(Hue) 및 색상 기울기(Hue-Gradient)를 기반으로 정의된 배경모델을 구축하고, 실시간으로 입력되는 영상과의 배경차분(background subtraction)기법을 이용하여 배경과 손을 구분한다. 추출된 손의 영역으로부터 18가지의 특징요소를 추출하고 이를 기반으로 다중클래스 SVM(Support Vector Machine) 학습 기법을 사용하여 손의 형태를 인지한다. 제안 기법은 색상 기울기를 배경 차분에 적용함으로써, 조명 환경이 배경 모델의 조명과 다르게 급격한 변화가 이루어졌을 때도 안정적으로 손의 윤곽정보를 추출할 수 있도록 하였다. 또한, 실시간 처리를 저해하는 복잡한 손의 특성정보 대신, OBB의 크기에 대하여 정규화된 두 개의 고유값과 객체 기반 바운딩 박스(OBB)를 구성하는 16개 세부 영역에서의 손 윤곽픽셀의 개수를 손의 특성정보로 사용하였다. 본 논문에서는 급격한 조명 변화 상황에서 기존 RGB 색상요소를 기반으로 하는 배경차분법과 색상을 기반으로 하는 배경차분법, 본 논문에서 제안하는 색상 기울기 기반 배경 차분법의 결과를 비교함으로써 제안 기법의 안정성을 입증하였다. 6명의 실험대상자의 1부터 9까지의 수지화 2700개의 영상으로부터 손 특성 정보를 추출하고 이에 대하여 훈련을 통한 학습 모델을 생성하였다. 학습모델을 기반으로 실험자 6인의 손 형태 1620개의 데이터에 대하여 인지 실험을 실시하여 92.6%에 이르는 손 형태 인식 성공률을 얻었다. In this paper, we present a robust hand recognition approach to sudden illumination changes. The proposed approach constructs a background model with respect to hue and hue gradient in HSI color space and extracts a foreground hand region from an input image using the background subtraction method. Eighteen features are defined for a hand pose and multi-class SVM(Support Vector Machine) approach is applied to learn and classify hand poses based on eighteen features. The proposed approach robustly extracts the contour of a hand with variations in illumination by applying the hue gradient into the background subtraction. A hand pose is defined by two Eigen values which are normalized by the size of OBB(Object-Oriented Bounding Box), and sixteen feature values which represent the number of hand contour points included in each subrange of OBB. We compared the RGB-based background subtraction, hue-based background subtraction and the proposed approach with sudden illumination changes and proved the robustness of the proposed approach. In the experiment, we built a hand pose training model from 2,700 sample hand images of six subjects which represent nine numerical numbers from one to nine. Our implementation result shows 92.6% of successful recognition rate for 1,620 hand images with various lighting condition using the training model.

Photovoltaic power supplied to the utility grid is gaining more and more visibility while the world’s powers demand is increases. Growing demand, advancements in semiconductor technology and magnetic materials such as high frequency inductor cores, has a significant impact on PV inverter topologies and their efficiencies, on the improvement of the control circuits on the potential of costs reduction. The user naturally wants to operate the Photovoltaic (PV) array at its highest energy conversion output by continuously utilizing the maximum available solar power of the array. The electrical system PV modules are powered by solar arrays requires special design considerations due to varying nature of the solar power generated resulting from unpredictable and sudden changes in weather conditions which change the solar irradiation level as well as the cell operating temperature. This paper, a mathematical model of a Photovoltaic (PV) cell used matlab-simulink environment, is developed and presented. The model is developed using basic circuit equations of the photovoltaic solar cells including the effects of solar irradiation and temperature changes. The main objective is to find the parameters of the nonlinear I–V equation by adjusting the curve at three points: open circuit, maximum power, and short circuit. the method finds the best I–V equation for the single-diode photovoltaic (PV) model including the effect of the series and parallel resistances – Key words : Photovoltaic system (PV), maximum power, PV array,PV cell DOI: http://dx.doi.org/10.11591/ijece.v2i1.117

Background/Objective: This paper presents a new digital control approach to track the Maximum Power Point (MPPT) for Potovoltaic (PV) systems. A photovoltaic power generation system requires an effective controller to overcome sudden changes to irradiance and to maximize its efficiency. Methods/Statistical Analysis: A new approach to MPPT based on voltage control was proposed, while a PI (Proportional Integral) controller was combined with the boost converter to adapt the duty cycle. Findings: The input voltage reference was adaptively perturbed with variable steps until the maximum power was reached. A state-space model was derived through the averaging method, with the control input being the duty ratio to regulate the pulse width modulator on the DC-DC boost converter. The proposed control scheme eliminated steady state oscillations around the working point area of the PV panel when there were rapid changes to the irradiance. Applications/Improvements: Furthermore, the PV system became more efficient, as proven by the sudden change in the radiation conditions for 0.3 seconds, where approximately 30% of energy could be saved. Keywords: Averaging Model, Boost Converter, Combination IP, Irradiance, New MPPT, Photovoltaic

The position of MPP of a solar panel changes with changes in weather conditions such as radiation, temperature and partial Shadow. In such a situation, several local MPPs and one GMPP are created in the P-V curve, and the common methods of MPPT(P&O, IC,…) have converged due to the failure to recognise the local MPP. They cause a lot of losses to the photovoltaic system. Therefore, in this article, it has been tried to provide a new method of particle swarm optimisation with variable coefficients, which in combination with the perturb and observe method can reach the GMPP in the fastest possible time. In this proposed method, we first reach the convergence of the GMPP using the VCPSO method, and after converging to it, we go to the P&O method and use P&O until significant changes are made in the system, and if observed Variations To re-examine the GMPP, we return to the VCPSO algorithm. The results of the simulation of the efficiency of the proposed method show that this method was able to reach the GMPP in the shortest time, and the efficiency of the photovoltaic system increases and the effects of partial shading are reduced.

The driver’s visual perception research to analyze pedestrian safety at twilight

We propose an edge-segment-based statistical background modelling algorithm to detect the moving edges for the detection of moving objects using a static camera. Traditional pixel intensity-based background modelling algorithms face difficulties in dynamic environments since they cannot handle sudden changes in illumination. They also bring out ghosts when a sudden change occurs in the scene. To cope with this issue, intensity and noise robust edge-based features have emerged. However, existing edge-pixel-based methods suffer from scattered moving edge pixels since they cannot utilize the shape. Moreover, traditional segment-based methods cannot handle edge shape variations and miss moving edges when they come close to the background edges. Unlike traditional approaches, our proposed method builds the background model from ordinary training frames that may contain moving objects. Furthermore, it does not leave any ghosts behind. Moreover, our method uses an automatic threshold for every background edge distribution for matching. This makes our approach robust to illumination change, camera movement and background motion. Experiments show that our method outperforms others and can detect moving edges efficiently despite the above mentioned difficulties.

To solve the problem of indoor surveillance, this paper is concerned about the framework that allows the detection of moving objects based on IP camera. The current background subtraction algorithms cannot correctly classify foreground and background if there are sudden illumination changes. To eliminate the influence of illumination changes, intensity flicker correction technique is employed before background subtraction in this paper. Meanwhile to reduce the false alarms further, the union of two separate detection results is employed. In the experimental results, both global illumination changes and local ones are included. The results show that the improved subtractor proposed in this paper performs better than ViBe both in the aspect of correct detection rate and false alarms.