Estimation Of Power Output Research Articles

Maximum power output prediction of HCPV FLATCON® module using an ANN approach

The estimation of the electrical power output and energy yield of high concentration photovoltaic (HCPV) modules is a hard task because of the many parameters involved. In this work, we propose the usage of an Artificial Neural Networks (ANN) method to estimate the maximum power output of a FLATCON® CPV-module manufactured by Fraunhofer ISE. The advantage of the ANN is that it is a part of the Artificial Intelligence Deep Learning domain, which makes it easy to model complex systems. A detailed knowledge about the underlying module technology is not necessary. In this work, various meteorological parameters are tested as inputs of the ANN including spectral matching ratios for considering the spectral variation of the solar irradiance to evaluate the maximum power output of the HCPV module under study. Eleven scenarios considering different combinations of input parameters have been investigated. It is demonstrated that the ANN gives excellent results and allows for an accurate prediction of the HCPV module’s instantaneous power output with only a few amount of data used on training the ANN. The model has been tested using the measured data set of a FLATCON® CPV-module located on the rooftop of the Fraunhofer ISE in Freiburg, Germany. The module has been electrically characterized outdoors in the period from April 2013 to April 2014. The accuracy of the proposed ANN approach was analyzed using this measurement data in combination with error metrics. It was found that the best agreement between the measured and the predicted maximum power output was achieved when using direct normal irradiance, wind speed, ambient temperature and spectral matching ratios as input. The normalized root mean square error for the maximum power output over one year is found to be between 2.2 and 4.6%. The deviation of the modelled energy yield to the measured one is in the range of 0.2–2.2%.

Leaping small fish out-power breaching whales

When Emmett Johnston from Queen's University, Belfast, UK, attached a motion sensing tag to a basking shark off the Irish coast, he expected the piece of kit to have a sedate ride. Nothing prepared Johnston and colleagues for the rollercoaster that the sensor went on in practice. ‘Having retrieved it, we watched the video and, to our amazement, the movie ended with the shark suddenly accelerating up through the water column, hitting the water surface at 5 m s−1 and breaching for about a second’, says Lewis Halsey from the University of Roehampton, London, UK. The massive fish was behaving more like cavorting gray whales or leaping salmon than the lethargic filter feeder it was meant to be. The team calculated that each leap could use up to 1/17th of the animal's daily metabolic budget (doi:10.1098/rsbl.2018.0537). However, after reviewing movies of breaching basking sharks filmed by observers on land, Halsey suspected that it might be possible to calculate the amount of energy an aquatic animal requires to make it into the air from the footage. ‘I thought there must be lots of video available of other aquatic species breaching’, says Halsey. Could he find a way to calculate how much power it takes for fish, whales and dolphins to surge out of the water from movies shot by citizen scientists?Halsey turned to the millennials’ TV channel of choice. ‘I kept searching YouTube with names of fish and cetaceans and various words for jumping until I couldn't find any more’, he chuckles, recalling how, eventually, he ended up with almost 30 clips of species ranging from a 20 cm long African tetra to a 13 m long humpback whale. Then Halsey teamed up with Gil Iosilevskii from the Technion, Israel, to calculate the animals’ sheer power and speed as they burst out of the water. ‘Gil did the hardcore maths’, recalls Halsey, adding, ‘He is one of the few people around with the capacity to develop the equations we used to estimate power output from the videos’. Halsey then estimated the animals’ sizes, measured the length of time each animal was airborne and determined several other values before plugging them into Iosilevskii's equations.Amazingly, the common bottlenose dolphins and spinner dolphins (both ∼2 m long) hit the highest take-off speeds of ∼10.7 m s−1 (38.5 km h−1), with the smaller species achieving progressively lower speeds; the slowest leaps were performed by the diminutive African tetra, which took off at only 4.4 m s−1. Meanwhile, the largest creatures, including the basking shark, great white shark and massive humpback whale hit speeds ranging from 5.8 to 9.1 m s−1; they didn't come close to challenging the dolphins’ speed. However, when the duo compared the peak muscle power of the animals, it was clear that the smaller species – including mackerel, mullet and the common bottlenose dolphin – generated the highest muscle powers of over 50 W per kg of muscle when jetting out of the waves. In contrast, the heftier great white shark, orca and humpback whale, managed less than 10 W per kg of muscle.Halsey suspects that the smaller species’ powerful departures ‘represents an estimate of a universal upper limit to power output’, adding that their pound-for-pound power was very similar to that of top human athletes performing right at their physical limits. ‘When these animals are breaching at their fastest, they might well be performing at maximum effort’, says Halsey.

Comparison of Electric Power Output Observed and Estimated from Floating Photovoltaic Systems: A Case Study on the Hapcheon Dam, Korea

An interest in floating photovoltaic (PV) is growing drastically worldwide. To evaluate the feasibility of floating PV projects, an accurate estimation of electric power output (EPO) is a crucial first step. This study estimates the EPO of a floating PV system and compares it with the actual EPO observed at the Hapcheon Dam, Korea. Typical meteorological year data and system design parameters were entered into System Advisor Model (SAM) software to estimate the hourly and monthly EPOs. The monthly estimated EPOs were lower than the monthly observed EPOs. This result is ascribed to the cooling effect of the water environment on the floating PV module, which makes the floating PV efficiency higher than overland PV efficiency. Unfortunately, most commercial PV software, including the SAM, was unable to consider this effect in estimating EPO. The error results showed it was possible to estimate the monthly EPOs with an error of less than 15% (simply by simulation) and 9% (when considering the cooling effect: 110% of the estimated monthly EPOs). This indicates that the approach of using empirical results can provide more reliable estimation of EPO in the feasibility assessment stage of floating PV projects. Furthermore, it is necessary to develop simulation software dedicated to the floating PV system.

Hydropower Potentials Estimation of Biliran Islands Based on Synthetic Aperture Radar Spatial Data Using Soil and Water Assessment Tool Simulation

Synthetic Aperture Radar (SAR) spatial data simulation using Soil and Water Assessment Tool (SWAT) was conducted to determine the hydropower generation potentials of three (3) major islands of Biliran Province in the Philippines. Results indicate that the dominant hydropower potentials of Biliran are of microhydro class with a total of 2,900 sites and an estimated power output of 35.37 megawatts. Minihydro and picohydro power potentials contributed only 2.8% and 20.5%, respectively, to the total of 46.15 megawatts hydropower potential in the main island of the Province. The second and third biggest islands, Maripipi and Higatangan, were found, through simulation, to have no significant river formation and, hence, no hydropower potential.

Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation

In this paper a genetic algorithm (GA) approach to design of multi-layer perceptron (MLP) for combined cycle power plant power output estimation is presented. Dataset used in this research is a part of publicly available UCI Machine Learning Repository and it consists of 9568 data points (power plant operating regimes) that is divided on training dataset that consists of 7500 data points and testing dataset containing 2068 data points. Presented research was performed with aim of increasing regression performances of MLP in comparison to ones available in the literature by utilizing heuristic algorithm. The GA described in this paper is performed by using mutation and crossover procedures. These procedures are utilized for design of 20 different chromosomes in 50 different generations. MLP configurations that are designed with GA implementation are validated by using Bland - Altman (B-A) analysis. By utilizing GA, MLP with five hidden layers of 80,25,65,75 and 80 nodes, respectively, is designed. For aforementioned MLP, k - fold cross-validation is performed in order to examine its generalization performances. The Root Mean Square Error ( R M S E ) value achieved with aforementioned MLP is 4.305 , that is significantly lower in comparison with MLP presented in available literature, but still higher than several complex algorithms such as KStar and tree based algorithms.

The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment

Driven by the energy auctions system, wind power in Brazil is undergoing a phase of expansion within its electric energy mix. Due to wind’s stochastic nature and variability, the wind measurement campaign duration of a wind farm project is required to last for a minimum of 36 months in order for it to partake in energy auctions. In this respect, the influence of such duration on a measure-correlate-predict (MCP) based wind resource assessment was studied to assess the accuracy of generation forecasts. For this purpose, three databases containing time series of wind speed belonging to a site were considered. Campaigns with durations varying from 2 to 6 years were simulated to evaluate the behavior of the uncertainty in the long-term wind resource and to analyze how it impacts a wind farm power output estimation. As the wind measurement campaign length is increased, the uncertainty in the long-term wind resource diminished, thereby reducing the overall uncertainty that pervades the wind power harnessing. Larger monitoring campaigns implied larger quantities of data, thus enabling a better assessment of wind speed variability within that target location. Consequently, the energy production estimation decreased, allowing an improvement in the accuracy of the energy generation prediction by not overestimating it, which could benefit the reliability of the Brazilian electric system.

Signal quality based power output prediction of a real distribution transformer station using M5P model tree

Forecasting the output power of transformer stations is a prominent analysis of power systems, which is key aspect of load balancing. Moreover, power forecasting may lead to maximized energy efficiency in the electrical grid. In this study, a fully operational real distribution transformer station which is resident in Istanbul is investigated. Transformer’s electrical characteristics which are strictly related with signal quality, are recorded every fifteen minute for four months, are employed to predict power output of the transformer. As a signal quality concern, a local electrical distribution company must supply stable and distortion free signal waveforms to the consumers where frequency-voltage stability, reduced THD values are satisfied. In order to estimate power output of the transformer, these parameters are subjected to well-known machine learning (ML) methods and consequently most effective methods are selected. In this paper M5P tree algorithm is promoted for total active power estimation of the analyzed transformer station. The M5P results have been compared with the recorded results using Root Mean Square Error (RMSE) and Correlation Coefficient (R) as 78.55 and 0.986, respectively. According to the results, the M5P model is performed quite efficiently in power estimation for the output power of distribution transformer station.

Simulation Study of Potential Impacts of Tidal Farm in the Eastern Waters of Chengshan Cape, China

The sea area east of Chenshan Cape has peak tidal current flows that exceed 2.3 m s−1, which make it a promising site for the development of tidal current energy. Before these resources can be exploited, a comprehensive assessment is needed of the potential environmental impacts of the extraction of this energy. In this paper, we describe our construction of a three-dimensional hydrodynamic model of the waters near Chengshan Cape, and verify the performance of the model using continuous data measured in situ. We modeled the potential impacts of the exploitation of these resources on the flow field by adding a momentum loss term in the governing equation of the model. Simulation results show that an assumed tidal farm with an estimated power output of 20.34 MW would have a significant impact on the surrounding water level, especially next to the farm, where fluctuation could reach 6 cm. The maximum drop in the flow velocity in the wake of the farm was predicted to be more than 0.8 m s−1, and this influence would extend 10 km downstream.

Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation

Accurate customer baseline load (CBL) estimation is critical for implementing incentive-based demand response (DR) programs. The increasing penetration of grid-tied distributed photovoltaic systems (DPVS) complicates customers’ load patterns, making the CBL estimation more difficult because the volatile actual load and the intermittent PV output power are coupled together. A PV-load decoupling framework is proposed in this paper to address the above issue. The basic idea is to decouple the actual load power and the PV output power, then estimate them separately. To this end, historical PV output power data of each individual DPVS is required. However, pure historical PV output power data is usually unavailable for small-scale DPVSs, since they are normally located behind the meter, thus only the net load (i.e. actual load power minus PV output power) data is metered. Therefore, this paper proposes a machine learning approach to disaggregate the output power of each individual DPVS from net load data. The proposed approach includes two stages: DPVS capacity estimation and PV output power estimation. The first stage consists of two steps. First, a net load curve optimal pairing-based feature extraction method is proposed to extract features from the discrepancy between two different net load curves of the customers under heterogeneous weather conditions. Second, a multiple support vector regression-based ensemble model with the input features extracted in the first step is established to estimate the DPVS capacity. In the second stage, the output power of each DPVS is estimated by its capacity multiplied by the output power of a standard DPVS. Case studies using a real dataset from Sydney indicate that the proposed approach shows a promising performance on PV output power estimation and can significantly improve the CBL estimation accuracy for customers with DPVSs.

Inverse heat transfer technique for estimation of focal flux distribution for a concentrating photovoltaic (CPV) square solar parabola dish collector

For a CPV system, prediction of focal flux distribution at the receiver area will give insight to more effective, energy efficient designs and to estimate power output. An experimental method for in-situ prediction of heat flux distribution profile using inverse heat transfer technique on a flat plate receiver for a CPV square parabolic dish is presented. An IR camera is used to measure the temperature of the concentrated receiver surface. The receiver domain is discretised into several heat flux elements and heat flux values for each grid is then estimated using the measured infrared (IR) pixel temperature and ordinary least square. A 3-D steady state heat conduction equation with convection and radiation heat loss boundary is regarded as the forward problem. The simulated temperatures generated from the solution of forward problem using the predicted heat flux distribution and measured temperature distribution are in close agreement. For validation purpose, the concentrated heat flux is also measured using Gardon and Schmidt-Boelter heat flux sensor. The peak predicted focal heat flux on the receiver is found to be 37.41 kW/m2 whereas the heat flux value measured by the flux sensor is 39.15 kW/m2 within the deviation of 4.4%.

Preliminary experimental comparison and feasibility analysis of CO2/R134a mixture in Organic Rankine Cycle for waste heat recovery from diesel engines

This paper presents results of a preliminary experimental study of the Organic Rankine Cycle (ORC) using CO2/R134a mixture based on an expansion valve. The goal of the research was to examine the feasibility and effectiveness of using CO2 mixtures to improve system performance and expand the range of condensation temperature for ORC system. The mixture of CO2/R134a (0.6/0.4) on a mass basis was selected for comparison with pure CO2 in both the preheating ORC (P-ORC) and the preheating regenerative ORC (PR-ORC). Then, the feasibility and application potential of CO2/R134a (0.6/0.4) mixture for waste heat recovery from engines was tested under ambient cooling conditions. Preliminary experimental results using an expansion valve indicate that CO2/R134a (0.6/0.4) mixture exhibits better system performance than pure CO2. For PR-ORC using CO2/R134a (0.6/0.4) mixture, assuming a turbine isentropic efficiency of 0.7, the net power output estimation, thermal efficiency and exergy efficiency reached up to 5.30 kW, 10.14% and 24.34%, respectively. For the fitting value at an expansion inlet pressure of 10 MPa, the net power output estimation, thermal efficiency and exergy efficiency using CO2/R134a (0.6/0.4) mixture achieved increases of 23.3%, 16.4% and 23.7%, respectively, versus results using pure CO2 as the working fluid. Finally, experiments showed that the ORC system using CO2/R134a (0.6/0.4) mixture is capable of operating stably under ambient cooling conditions (25.2–31.5 °C), demonstrating that CO2/R134a mixture can expand the range of condensation temperature and alleviate the low-temperature condensation issue encountered with CO2. Under the ambient cooling source, it is expected that ORC using CO2/R134a (0.6/0.4) mixture will improve the thermal efficiency of a diesel engine by 1.9%.

About estimation of base station output power in GSM like systems under real conditions

The output power of traffic channels in one cell of GSM like systems is estimated in this paper. We consider the real case: the number of users is much higher than the number of channels, the output power of one channel depends on the cube of the distance between a mobile user and the base station, and the distribution of users in the cell is uniform. We derive the expressions for cumulative distribution of output power of one channel and for the mean output power of the whole base station. Results of the calculation are confirmed by computer simulation.

Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient

This work presents a computational method for the simulation of wind speeds and for the calculation of the statistical distributions of wind farm (WF) power curves, where the wake effects and terrain features are taken into consideration. A three-parameter (3-P) logistic function is used to represent the wind turbine (WT) power curve. Wake effects are simulated by means of the Jensen’s wake model. Wind shear effect is used to simulate the influence of the terrain on the WTs located at different altitudes. An analytical method is employed for deriving the probability density function (PDF) of the WF power output, based on the Weibull distribution for describing the cumulative wind speed behavior. The WF power curves for four types of terrain slopes are analyzed. Finally, simulations applying the Monte Carlo method on different sample sizes are provided to validate the proposed model. The simulation results indicate that this approximated formulation is a possible substitute for WF output power estimation, especially for the scenario where WTs are built on a terrain with gradient.

Design, modeling and optimization of an m-shaped variable reluctance energy harvester for rotating applications

The variable reluctance principle can be used to convert rotational kinetic energy into electrical energy, creating a Variable Reluctance Energy Harvester (VREH) based on electromagnetic induction. This can be used to implement self-sustaining wireless sensors in rotating applications. In this paper, we present and investigate a novel design of a VREH with high volumetric power density that targets low-speed rotating applications. The design uses an m-shaped pole-piece and two opposing magnets. We theoretically analyze key design parameters that influence the VREH’s output power, and relate these parameters to geometrical design factors of the proposed structure. Key design factors include the coil height, the permanent magnet height and the tooth height. A method based on numerical simulations is introduced, enabling to determine the optimal geometrical dimensions of the proposed structure under given size-constraints. The results demonstrate that the method leads to optimal structure configurations, which has been evaluated for different cases and is verified experimentally. Good agreement between numerical simulations and experiments are reported with deviations in output power estimation below 3%. The optimized m-shaped VREH, moreover, provides output power levels sufficient for wireless sensor operation, even in low-speed rotating applications.

Possibility of Using Stirling Engine as Waste Heat Recovery – Preliminary Concept

Growing consumption of primary fossil fuels and massive discharge of pollutants are some of the results caused by the world’s growing population, and eventually the enlarging energy demand. It is therefore the main concerns that the developing world must face nowadays are the energy shortfall and the environmental destruction. And for these valid reasons the awareness of reusing the low-grade heat has captivated researchers in recent years. Due to its unique features, Stirling engines, is a powerful candidate to recover the waste heat by converting it into power. However, Stirling engine shows a drastic performance penalization if connected with lower temperature heat sources and therefore research has to be done to increase the performance of the heat transfer in the Stirling engine. Exhaustive research has been done by many investigators to enhance the heat transfer characteristics inside the tube heat exchangers. However, the areas related to outer tube geometries with different materials and different fins attachment have not yet been explored and this part is the important factor for an external combustion engine to enhance the heat transfer. Thus, the development of Stirling engine as waste heat recovery needs to focus on identification of heat transfer enhancement methods that can be applied at the outer part of tubular heater in order to achieve an optimum performance of the engine. Starting from a comprehensive review of Stirling engine, this paper presented a rigorous derivation of a novel waste heat recovery system using Stirling engine with large power that can benefit the environment and in line with national GTMP. Such result will be very useful in the preliminary design of a waste heat recovery system using Stirling engine and can be used in the estimation of power output for many applications.

A novel method for autonomous remote condition monitoring of rotating machines using piezoelectric energy harvesting approach

This paper presents a novel autonomous method for condition monitoring of rotating machines during operation based on radio frequency (RF) pulse transmission using energy harvesting from operational vibration. An energy harvesting unit is designed to generate and rectify the energy harvested from the machine vibration using Voltage Multiplier (VM) circuit and to store the energy into a capacitor. Then, this energy harvesting unit runs a smart system consisting of a microcontroller and the RF transmitter designed to send a pulse at specific capacitor voltage. A pulse-based condition monitoring approach is introduced which monitors the state of the machine during the operation. In order to estimate power output of the piezoelectric harvester for a realistic vibration signal, the Fourier Transform concept for signal decomposition is incorporated into the well-known electromechanical distributed parameter model. Using experimental data, performance of this autonomous condition monitoring system is tested for a water pump at different conditions. To do so, acceleration data from a centrifugal water pump are acquired with an accelerometer, which then decomposed into a series of harmonics using Fast Fourier Transform. Then using analytical distribute model, a bimorph energy harvester with two Piezoceramic layers is optimized to generate maximum power from the water pump vibration. Consequently, the condition monitoring of the water pump is performed using the presented pulse-based approach. Results of this study show that, the fault diagnosis can be performed autonomously by applying the pulse-based method presented in this work, and by using the piezoelectric harvesting device as an energy source.

Preliminary design of a 100\xa0MW-net ocean thermal energy conversion (OTEC) power plant study case: Mentawai island, Indonesia

Ocean thermal energy conversion is one of the promising renewable energy resources yet relatively unexplored due to its high capital cost for being utilized in commercial scale. In the aim to reduce the capital cost, this paper introduces a concept design of the floating structure from a converted oil tanker ship. To propose the design process, the general principles of designing a converted tanker FPSO is adapted and then modified to deal with ocean thermal energy conversion (OTEC) characteristic. In the design process, the arrangement of the OTEC layout is carried out by constraint satisfaction method and the prospective floating structure size is varied using Monte Carlo simulation. The variables in the design process consist of the velocities of cold water and warm water transport, the size of the plantship, and the location of the OTEC equipment to the seawater tank. Constraints are introduced as allowable border to determine the acceptability for particular case including the provided space and buoyancy, and the net power output estimation. The results show that the ‘typical’ size of a Suezmax oil tanker ship is the optimum one for the plantship with the velocity of the water transport of 2–3 m/s. The general arrangement is also conceptualized in this paper.

A PV Power Output Estimation Method using Covariance between Sampled PV Power Output data and Power Flow

A PV Power Output Estimation Method using Covariance between Sampled PV Power Output data and Power Flow

Theoretical Study of A Three-Swing Blades Rotary Engine

The innovative eccentric swing blade rotary engine is internally divided into a compression section, a combustion chamber, and a power section. With the three swing blades on each rotor, the swing blade comprises a cylindrical roller mounted to a front end and a curved back, and each of the roller is provided, at an inner side, with a support device that is capable of sustaining a counteracting force applied by a cylinder wall to the roller. After completing the combustion of the mixed gas in the combustion chamber, the generated high-pressure gas is injected into power section and push the swing blades rotation, resulting in power. In this study, the theoretical model has been established to calculate the trajectory/the tip speed of the swing blade, pressure variation, compression torque and to predict the performance of the rotary engine. For a prototype 120 cc rotary engine, it can provide an estimated power output between 6.26 to 50.16-hp at about 1000 to 8000 rpm.

Tracking error compensation technology for coherent tracking system

A method for compensating the tracking boresight error by providing additional jitter for the signal light is proposed. The relationship between jitter angle and system performance is analyzed, which shows the sensitivity attenuation is within 0.1 dB when the angle is within 0.2–1.2 μrad. The optimal mathematical model of output power and compensation angle estimation is established. And the simulation results show the mixing efficiency of the coherent optical communication system is well improved.