Fast Tracking Time Research Articles

A Novel EPSO Algorithm Based on Shifted Sigmoid Function Parameters for Maximizing the Energy Yield from Photovoltaic Arrays: An Experimental Investigation

This paper presents a new method for maximizing the energy production of photovoltaic (PV) arrays, utilizing the Enhanced Particle Swarm Optimization (EPSO) algorithm. In contrast with the classical PSO, the proposed EPSO algorithm employs shifted sigmoid functions in order to adapt the acceleration coefficients and the inertia weight instead of using constant coefficients. The EPSO algorithm proves its effectiveness even in challenging either condition, such as abrupt variations in solar irradiance or instances of partial shading conditions (PSCs). The proposed EPSO-based Maximum Power Point Tracking (MPPT) controller has undergone testing and a comparative analysis alongside well-known metaheuristic algorithms, which include the PSO, Bat Algorithm (BAT), Grasshopper Optimization Algorithm (GOA), and Grey Wolf Optimizer (GWO). The results obtained through simulations consistently demonstrate that the EPSO-based MPPT method surpasses other metaheuristic approaches in terms of energy yield and the speed at which it tracks the Maximum Power Point (MPP) under PSCs. In addition, the proposed EPSO algorithm demonstrates robustness, maintaining consistent tracking of the MPP during sudden solar radiation changes. Experimental validation on a real PV system affirms these findings, demonstrating that the EPSO algorithm surpasses its counterparts by achieving a high MPPT efficiency of approximately 99.19% in a fast-tracking time of 2.4 s while maintaining minimal power oscillations of around 2.04 %.

Optimized Perturb and Observe Techniques for Sudden Irradiance and Temperature Change

Abstract: Solar Power generation plays vital role in RES due to its natural abundance and environment friendly. The efficiency of the PV System depends on the more optimized MPPT Technique. Several methods for extracting GMPP are discussed that works better on steady state oscillations and fast tracking time. In this paper all recent techniques for MPPT are discussed and more efficient algorithm and simple in approach is revealed. This paper tries to find more efficient algorithm with fast tracking speed and optimized duty cycle.

An Unusual chaotic system with pure quadratic nonlinearities: Analysis, control, and synchronization

An unusual chaotic oscillator consisting of two parameters, six quadratic nonlinear terms, and eight unstable equilibrium points that form vertices of a parallelepiped has been investigated in this work. The dynamics of the system were explored using bifurcation diagrams, time histories, phase plane trajectories, Lyapunov exponents, and basins of attraction. The results indicated that the system possesses a chaotic self-excited basin of attraction, manifesting as a connected region in space surrounded by a basin of unbounded motion. An analog simulation for the considered system has been demonstrated in the form of an electronic circuit using Multisim software. The simulation results confirmed the excellent correspondence with the numerical analysis, which ultimately proves the feasibility of the considered chaotic oscillator. Then, chaos control and master-slave synchronization were addressed using a sliding-mode control strategy. In addition, a comparison with different synchronization techniques has been introduced. The presented analyses show that sliding-mode synchronization and predefined time synchronization are the two superior controllers in terms of bounded control signals and fast-tracking time.

A novel metaheuristic MPPT technique based on enhanced autonomous group Particle Swarm Optimization Algorithm to track the GMPP under partial shading conditions - Experimental validation

In order to maximize the benefit of solar panels, it is crucial to enhance the operating efficiency of PV systems. Usually, the PV arrays are exposed to harsh outdoor operating conditions that affect their operating efficiency. Extraction of global maximum power point (GMPP) among the other local MPPs (LMPPs) from the distorted characteristics of the PV array is considered the main challenge of PV system controllers under partial shading conditions (PSCs). Classical MPP tracking (MPPT) techniques have incompetent performance in tracing the GMPP under the PSCs of PV arrays. Therefore, many metaheuristics-based MPPT techniques are developed by researchers to defeat the problems related to the PSCs and cope with GMPP and other LMPPs. Among all metaheuristic algorithms, Particle Swarm Optimization (PSO) and its modified versions are the most common optimization methods owing to their easy implementation, simplicity, and low computational burden. On the other hand, the standard PSO (SPSO) algorithm suffers from low convergence speed, and difficulty in tuning its parameters, and it may be trapped in LMPP under fast changes in solar radiation and/or PSCs. In order to tackle these issues, a novel metaheuristic MPPT technique based on an Enhanced Autonomous Group PSO (EAGPSO) algorithm is proposed. A comparative study between EAGPSO and other versions of PSO techniques is performed to emphasize the efficacy of the EAGPSO technique. The simulation results demonstrate that the proposed EAGPSO algorithm is superior to the counterpart versions of the PSO algorithms in terms of fast-tracking time, high obtained power, and high energy efficiency. Experimental validation of the EAGPSO technique has been conducted using an array of 3*3 series–parallel PV panels under PSC. The EAGPSO algorithm results have been compared with SPSO and AGPSO techniques. The experimental results show that, among the investigated MPPT techniques, the proposed EAGPSO technique has the highest MPPT efficiency of 98.6% and the lowest power oscillations of about 2.03% of the tracked power with a tracking time of 2.6 s.

Recursive bit assignment with neural reference adaptive step (RNA) MPPT algorithm for photovoltaic system

Recent research has focused on photovoltaic (PV) systems due to their important properties. The efficiency of the PV system can be enhanced by many Maximum Power Point Tracking (MPPT) algorithms proposals. MPPT algorithms are used to achieve maximum PV output power by optimizing the duty cycle of the DC–DC buck/boost converter. This paper introduces an RNA algorithm as an efficient MPPT algorithm for the photovoltaic system. This proposed RNA algorithm consists of two main segments. The first segment is an artificial neural network for generating reference power. The second segment is a proposed Recursive Bit Assignment (RBA) network to allow variable step size of the boost converter duty cycle. The instant PV power adopts the RBA network to produce the variable duty cycle increment value. Additionally, the neural network is implemented in such a way to obtain the best performance. Many simulation results using MATLAB to test the system performance are presented. The performance characteristics of the photovoltaic system with variable irradiance and variable temperature are simulated. From results, the proposed RNA algorithm achieves fast tracking time, high energy efficiency, true maximum power point and acceptable ripple. Additionally, comparisons between the RNA algorithm and other related algorithms such as Perturb and Observe, the Neural Network and the Adaptive Neural Inference System Algorithms are executed. The proposed RNA algorithm achieves the best performance in all case studies such as; irradiance profile variation, severe temperature and irradiance diversions, and partial shading conditions. Besides, the experimental circuit of the PV system is also presented.

Hybrid photovoltaic maximum power point tracking of Seagull optimizer and modified perturb and observe for complex partial shading

<span>Due to natural randomness, partial shading conditions (PSCs) to photovoltaic (PV) power generation significantly drop the power generation. Metaheuristic based maximum power point tracking (MPPT) can handle PSCs by searching PV panels’ global maximum power point (GMPP). However, trapped at local maxima, sluggishness, continuous power oscillations around GMPP and inaccuracy are the main disadvantages of metaheuristic algorithm. Therefore, the development of algorithm under complex PSCs has been continuously attracting many researchers to yield more satisfying results. In this paper, several algorithms including conventional and metaheuristic are selected for candidate, such as perturb and observe (P&O), firefly (FF), differential evolution (DE), grey wolf optimizer (GWO) and Seagull optimizer (SO). From the preliminary study, SO has shown best performance among other candidates. Then, SO is improved for rapid global optimizer. Modified variable step sizes perturb and observe (MVSPO) is applied to enhance the accuracy tracking of SO. To evaluate the performances, high complexity multipeak partial shading is used to test the algorithms. Statistical results are also provided to analyze the trend of performances. The proposed method performances are shown better fast-tracking time and settling time, high accuracy, higher energy harvesting and low steady-state oscillations than other candidates.</span>

Optimization and implementation of a photovoltaic pumping system using the sine–cosine​ algorithm

Recently, a particular and considerable attention has been paid to photovoltaic pumping systems in reasoning their operation based on renewable and clean energy. In this sense, we suggest an optimum photovoltaic pumping system based on the monitoring of the maximum power point by the sine cosine optimization algorithm in the meteorological conditions of the village of Ain Beda which is located in the region of Fez–Meknes, Morocco. The objective of the proposed system is to ensure operation at maximum power during the pumping process by directly and intelligently controlling the photovoltaic pumping system by the sine–cosine algorithm under various climatic conditions in this region. A comparative study at the statistical and qualitative levels was carried out on the one hand between the proposed method and two other conventional algorithms such as perturb and observe and fuzzy logic control and on the other hand with other metaheuristic algorithms. The simulation results show the efficiency of the proposed photovoltaic pumping system in terms of fast-tracking time, accelerated convergence speed, very low initial oscillation, very low steady-state oscillations around the maximum power point, the dynamic performance of the flow which reaches 31.15 m3/h, and efficiency of the photovoltaic pumping system which reaches 84.88% in different weather conditions. Moreover, the suggested system is validated following the V-cycle validation and verification process by integrating the generated embedded software on a “Raspberry Pi 2 Model B” embedded board.

Development and real time implementation of intelligent holistic power control for stand alone solar photovoltaic generation system

This paper focusses on real-time implementation of an intelligent Holistic Power-Control system which is suitable for uniform, dynamic-irradiance and shaded-conditions for a stand-alone solar-PV system. The model consists of two intelligent Maximum Power Point Tracking(MPPT) control algorithms under different environmental conditions and a novel shading-detection mechanism. A fuzzy logic MPPT system with rule-base developed from real-time solar panel have been implemented. A partial shading detection mechanism based on power loss generated from solar PV array without any sensors is incorporated in the real-time platform. A fuzzy-logic Sweep-Inclusion algorithm is proposed to reach global maximum power point with fast tracking-time under partially-shaded conditions. To test the performance, real-time PV-system with data acquisition using LabVIEW is developed. The standalone solar-PV system is benefitted by the proposed-method by its less tracking-time and more efficiency, accurate partial shading detection through modified power loss scheme and Fuzzy Logic Sweep-Inclusion algorithm for identifying the global-MPPT point.

A maximum power point tracking based on levy flight optimization

This paper proposes a Levy flight global maximum power point tracking for solar photovoltaic (PV) system under partial shading conditions. The proposed method comes with merits such as simplicity, fast response and free of oscillation. This algorithm uses random search over the exploration space and compares the previous and current states to obtain the best solution. For evaluation and comparative analysis, performance of the proposed method is also measured against Perturb and Observe (P&O) and Particle Swarm Optimization (PSO). All three algorithms are simulated in MATLAB/Simulink environment. Simulation results are satisfactory over the conducted tests under uniform and non-uniform irradiance. The proposed algorithm is able to track global maximum power point (GMPP) under partial shading conditions with fast tracking time and zero ripple at steady-state.

A Capacitor-Less Ripple-Less Hybrid LDO With Exponential Ratio Array and 4000x Load Current Range

A hybrid low-dropout regulator (LDO) with exponential-ratio array (ERA) and analog based transient voltage compensation is implemented in 65-nm CMOS. The hybrid LDO (HLDO) is capable of reducing a voltage drop upon load change by more than 70%, and the ERA enables fast tracking time, enlarging dynamic range of the digital loop. The HLDO achieves 99.7% current efficiency with the output dynamic range of 0.01-to-40 mA (4000x) and obtains the transient output voltage drop of 55 mV for a load current jump of 0.55 mA from 20 $\mu \text{A}$ . When both the dynamic and the static performances are considered, the proposed HLDO shows the state-of-the-art performance among other recent digital LDOs and HLDOs.

Literature Survey of SAR Algorithm in Photovoltaic System

Every solar energy harvester systems have got two sources of energy loss: the MPPT circuit and the dc–dc converter. To increase the efficiency of the PV energy harvester, the energy losses from the MPPT circuit and the dc–dc converter need to be minimized. Here a new MPPT algorithm called successive approximation register is introduced. This MPPT algorithm has got a power down mode and a fast tracking time, to achieve low power consumption and energy savings. With this MPPT algorithm energy losses from the MPPT circuit can be minimized and this technique can be greatly applicable to low power application systems mainly as well as for high power application. DOI: http://dx.doi.org/10.11591/ijece.v4i2.4557

SAR Algorithm Method in Photovoltaic System using MPPT

Every solar energy harvester systems has got two sources of energy loss: the MPPT circuit and the dc–dc converter. To increase the efficiency of the PV energy harvester, the energy losses from the MPPT circuit and the dc–dc converter need to be minimized. Here a new MPPT algorithm called successive approximation register is introduced. This MPPT algorithm has got a power down mode and a fast tracking time, to achieve low power consumption and energy savings. With this MPPT algorithm energy losses from the MPPT circuit can be minimized and this technique can be greatly applicable to low power application systems mainly as well as for high power application. DOI: http://dx.doi.org/10.11591/ijpeds.v3i4.4556

An Energy-Efficient Fast Maximum Power Point Tracking Circuit in an 800-μW Photovoltaic Energy Harvester

An energy-efficient maximum power point tracking (MPPT) circuit with a fast-tracking time for use with 800-μW PV energy harvesters is presented in this paper. The proposed MPPT circuit uses a successive approximation register MPPT algorithm, which has a power down mode and a fast tracking time, to achieve low power consumption and energy savings. The prototype MPPT circuit, which consists of analog-based circuits, has been implemented and fabricated in a 0.35-μm BCDiMOS process. The MPPT core occupies an area of 3 mm2 and consumes 4.6 μW of power. The tracking time is reduced by 69.4% and the stored energy is increased by 31.4% as compared to the conventional hill climbing-based MPPT algorithm under indoor conditions.

Management of post-strabismus nausea and vomiting in children using ondansetron: a value-based comparison of outcomes

This study evaluated the clinical efficacy and cost-effectiveness of prophylactic ondansetron versus early ondansetron treatment in the management of postoperative nausea and vomiting (PONV) in children undergoing strabismus repair using clinically meaningful outcomes and value-based principles. One hundred and fifty children were randomly assigned to either prophylactic (P) or early symptomatic treatment only (T) group (n = 75). Children in group P received ondansetron 100 micrograms kg-1 i.v. and those in group T received placebo at the end of the procedure. After surgery, at the earliest sign of nausea or vomiting, children in both groups received ondansetron 100 micrograms kg-1 i.v. Besides the incidence of PONV, non-surrogate (fast tracking time, duration of stay in the postanaesthesia care unit (PACU) and parental satisfaction scores), therapeutic (numbers needed to prevent and treat) and pharmacoeconomic (cost to benefit a child and cost per PONV-free child) outcome measures were evaluated. The incidences of PONV in the immediate, early, late and first 24-h periods were significantly less in group P (20, 12, 19 and 35% respectively) than in group T (37, 29, 47 and 72%, P < 0.05). Time to achieve fast-track eligibility and duration of PACU stay were significantly shorter in group P (P < 0.001). Children in group P had superior mean (SD) parental satisfaction scores (8.2 (1.8)) compared with those in group T (6.8 (1.7), P < 0.001). The number needed to prevent PONV was 2 and the number needed to treat PONV was 9. The cost to benefit a child was more than fourfold less and the cost per PONV-free child was 35% less in group P. Compared with early symptomatic treatment with ondansetron, prophylactic ondansetron shortened fast-tracking time and duration of PACU stay and improved parental satisfaction and therapeutic outcomes at a lower direct cost.

The minimum effective intravenous dose of ondansetron for prevention of postoperative nausea and vomiting after adenotonsillectomy in dexamethasone pretreated children

Postoperative nausea and vomiting (PONV) remains a distressing and common problem after tonsillectomy with an incidence ranging from 40-73% in those who did not receive prophylactic antiemetic. This study was done to identify the minimum effective IV dose of ondansetron to decrease the incidence and severity of PONV in the dexamethasone (150ug/kg) pretreated children undergoing adenotonsillectomy. In this prospective, randomized, doubleblinded, placebo-controlled study, 150 children (3-12 years old) received dexamethasone 150g/kg IV (maximum 8mg) premedication were randomly assigned to receive either placebo (saline) or ondansetron in a dose of 25, 50, 75 or 150g/kg IV immediately after induction of anesthesia. All children received standardized perioperative care, including surgical and anesthetic techniques, IV fluid, postoperative analgesic and rescue antiemetic (RAE). The incidence and severity of PONV were recorded in a standardized fashion at the intervals 0-2, 2- 12 and 12-24h postoperatively. The time to first postoperative analgesic, total analgesic consumptions, the need for rescue antiemetic (RAE), the fast tracking time (FTT), the time to first oral intake and parent’s satisfaction score were recorded as clinically true outcome measures. The five treatment groups were similar with respect to patients’s characteristics and operative data. There was no significant difference with respect to the incidence (P>0.05) or severity (P>0.05) of PONV between the placebo and 25g/kg ondansetron group during the study period (0-24h). The incidence of early (0-2h), delayed (2-12h), and late (12-24) PONV were significantly less in the 50 (P 0.05) or the severity (P>0.05) of PONV between the 50, 75 and 150g/kg ondansetron groups. The time to first postoperative analgesic, the total postoperative analgesic consumptions, the need for RAE, the time to first oral intake and the fast tracking time (FTT) were significantly less (P<0.05) in children who received 50, 75 and 150 g/kg ondansetron in comparison with placebo. The parent’s satisfaction scores were significantly high (P<0.05) for those children who received ondansetron in a doses of 50g/kg or more compared with placebo. There was no significant difference with respect to the clinically true outcome measures in children who received ondansetron in dose of 50g/kg or more. In conclusion, ondansetron 50g/kg IV was the minimum effective IV dose to decrease the incidence and severity of PONV in dexamethasone (150g/kg IV) pretreated children undergoing adenotonsillectomy. This dose was associated with a significant reduction in the time to first postoperative analgesic, total analgesic consumptions, the need for rescue antiemetic (RAE), the time to first oral intake, the fast tracking time (FTT) and a high parent’s satisfaction scores. Increasing the dose of ondansetron to 150g/kg provided no significant benefits in reducing the incidence or severity of PONV in dexamethasone (150g/kg IV) pretreated children undergoing adenotonsillectomy

Dexamethasone is a cost-effective alternative to ondansetron in preventing PONV after paediatric strabismus repair

This study evaluated the antiemetic efficacy, cost-effectiveness and clinical utility of prophylactic ondansetron and dexamethasone compared with placebo in the prevention of postoperative nausea and vomiting (PONV) in 135 children (2-15 yr, ASA I-II) undergoing strabismus repair. After induction with halothane and nitrous oxide in oxygen or i.v. thiopental, the children received i.v. dexamethasone 1 mg kg(-1) to a maximum of 25 mg, ondansetron 100 microg kg(-1) to a maximum of 4 mg or placebo (n=45). Episodes of PONV were recorded for the first 24 h after the operation. True outcome measures (parental satisfaction score, duration of stay in the postanaesthesia care unit and fast tracking time), therapeutic outcome measures (number needed to prevent (NNTP) PONV) and the cost to benefit a child with each drug were analysed. The incidence and severity of PONV in the first 24 h were significantly less in the dexamethasone and ondansetron groups than in the placebo group (P<0.05). The incidence (P=0.04) and severity (P=0.03) of PONV at the 6-24 h epoch were significantly less in the dexamethasone group than in the ondansetron group. Recovery time (P=0.07), fast tracking time (P=0.6), parental satisfaction scores (P=0.08) and NNTP PONV were comparable (NNTP=2) in both the ondansetron and the dexamethasone group. The cost to benefit a child with dexamethasone was approximately 22 times less than that of ondansetron.