Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high ...penetration level of photovoltaic (PV) generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM) technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC) maximum power point tracking (MPPT) technique that is based on proportional integral (PI) controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN), ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO) techniques and their performance are compared with existing models like back propagation (BP) forecasting model.
A new hybrid model combining the kernel functions along with the random vector functional link neural network (RVFLN) is proposed in this paper for an effective solar power prediction. The ...conventional RVFLN is known for its fast learning speed, simple architecture and good generalization capabilities and allows direct connection between input and output nodes along with nonlinear enhancement nodes with random weights. However, the bottleneck of selecting the number of hidden enhancement nodes and mapping functions is still a challenging problem. To overcome these deficiencies of the conventional RVFLN, kernel functions are used for both the direct links and the hidden nodes to provide better stability, generalization and regression accuracy. Instead of using a single kernel for the enhancement nodes, this paper proposes an optimal kernel function that comprises a linear combination of weighted local kernel and a global kernel to improve the prediction accuracy of the solar power generation. This optimal kernel will be known as multi-kernel RVFLN (MK-RVFLN), and its parameters are optimized using an efficient metaheuristic evaporation-based water cycle (EVWCA-MKRVFLN) to provide accurate prediction of solar power. To validate its superior prediction accuracy, two solar power plants of 25 and 100 MW capacity in the states of New York and California are considered for 5-min- and 60-min-ahead prediction in the months of January, April, July and October. The result analysis shows that the MK-RVFLN algorithm attains better performance than many other techniques.
15 min ahead irradiance prediction in sunny weather.
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•A new robust Wavelet Kernel ELM is used for solar irradiation forecasting.•VMD and EMD are used to decompose the solar ...irradiation time series data samples.•To reduce the computational overhead the size of the kernel matrix is reduced.•A variable weighting factor is used for each error residual for robustness.•Solar irradiation prediction during changing weather condition is also presented.
In this paper a new hybrid method has been implemented by combining Variational Mode Decomposition (VMD) and a new low rank robust kernel based Extreme Learning Machine (RKELM) for solar irradiation forecasting. This hybrid model presents an efficient and effective short term solar irradiation prediction approach using the historical solar irradiation data. The original non-stationary time series data is decomposed into various modes using VMD approach. The proposed VMD-RMWK (VMD based reduced Morlet Wavelet Kernel extreme learning machine) method is used to predict the solar irradiation of an experimental 1 MW solar power plant in Odisha, India. Different time intervals of 15 min, 1 h and 1 day ahead in different weather conditions are considered for forecasting purpose. The VMD technique decomposes the original nonlinear irradiation into a set of Variational Mode Functions (VMFs), and the extracted VMFs are used to train the kernel based robust ELM. Comparison with empirical mode decomposition (EMD) based low rank kernel is also presented in this paper. As a new contribution to the previously performed literature survey this paper presents a more accurate solar irradiation prediction paradigm for distinctive weather conditions, and different time intervals varying from very short duration of 15 min to one day ahead. Also to improve the reliability of the KELM and to make it robust under noisy conditions and the presence of outliers in the data, a weight loss matrix has been derived using a non-parametric kernel density estimation method and incorporated in the new formulation (RKELM). A typical solar power experimental solar power station in India has been taken for detailed study showing clearly the accuracy and robustness of the proposed approach. For cross validation of the proposed model, solar irradiation data from a solar power plant located in the state of Florida has been implemented.
This paper comprises of an effective Local Energy Management System (LEMS) implementing the Generalized Power Prediction Model (GPPM) for the uncertain performance of various Distributed Generations ...of a microgrid. The primary DGs considered to compensate intermittency is Wind Power Generation System (WPGS) and Photovoltaic (PV) along with Battery Energy Storage (BES). Solar and wind power are considered here as prediction targets and to cope with the intermittent nature of the Renewable Energy Sources (RESs), Power calculation from the RES data is included in the proposed prediction model. In order to establish arobust reduction in the predicted error, areal-time Online Sequential Kernel based Robust Random Vector Functional Link Network (OS-KRRVFLN) prediction algorithm is developed. Here, the initial randomness of the proposed OS-KRRVFLN is reduced (robust performance) by Hampel’s cost function (eP based) minimization. The PV-BES and WPGS based DGs are designed to be operated under certain uncertainties like PV arc, bus faults etc. as uninterrupted power supply with minimum grid power dependency. An efficient Distributed Adaptive Droop (DAD) control is thus opted as Primary Controller (PC). Addictiveness of DAD’s operational region is estimated while compensating eP”-. The coordination of GPPM with DAD-PC based accurate reference estimation for Independent DG Controllers (IDGCs) operation is considered as LEMS operation.
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•A novel local Energy management system for PV–Wind–Battery microgrid is proposed.•A robust randomized OS-RVFLN is used for uncertain power prediction from renewable sources.•An efficient Distributed Adaptive Droop control is thus opted as Primary Controller.•The prediction error compensated droop controllers are used for energy management.•Hardware in a loop verification with D-Space for DGs uncertainty is highlighted.
This study proposes an efficient local energy management system (LEMS) based on the generalised power prediction model for the uncertain operation of renewable distributed generations (DGs)-based ...microgrid. Photovoltaic with battery energy storage, and wind power generation are considered as primary DGs to compensate intermittency. Conventional direct power prediction models are limited to specific DG applications, where the plant data acquisition system is a necessity. Solar irradiance and wind speed are considered here as prediction targets to cope with such additional expenditure for a microgrid. To ensure a robust reduction in prediction error (ep), a short-term prediction model is developed by virtue of the proposed robust regularised random vector functional link network. A maximum-likelihood estimator using Huber's cost function is employed to attain the robustness of this model. Further, a direct renewable energy source-power calculation is opted to address model accuracy under local uncertainties. The LEMS operation is completed by compensating ep with distributed adaptive droop-based primary controllers for multi-DG based microgrid. To ensure the performance of the prediction model, solar irradiance, wind speed and power at different atmospheric conditions (seasonal volatility) and time span (i.e. 5, 10 and 60 min) have been implemented in MATLAB and real time.
In this paper, a new and efficient hybrid empirical wavelet transform (EWT)-based reduced robust Mexican hat wavelet kernel ridge regression (RMHWK) model is proposed to achieve both point and ...interval forecasting of solar power in a smart grid scenario. Initially, the actual nonlinear solar power data series was decomposed by the EWT method. A reduced robust kernel ridge regression (RKRR) approach was incorporated that shows a notable decrease in training time without appreciable loss in forecasting accuracy. The reduction in the size of the kernel matrix was achieved by selecting a set of random support vectors from the training data set. For validating the superior performance of the proposed EWT-RMHWK forecasting model, a numerical experimentation implementing a real-time data set of 1 MW solar power plant (Odisha, India) as well as an online historical data set (Florida, USA) was considered and compared with other hybrid models using either empirical mode decomposition- or wavelet decomposition-based RKRR and EWT-ELM, etc. The kernel parameters were optimized with the chaotic water cycle algorithm to boost the performance of the proposed prediction model. Further, the proposed EWT-RKRR method was used to construct prediction interval forecasting with three different confidence levels with 90%, 95%, and 99% for Florida solar power plant using different time horizons of 15 min, 1 h, and 1 day, respectively.
Solar power forecasting using a hybrid EMD-ELM method Majumder, Irani; Behera, Manoja Kumar; Nayak, Niranjan
2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT),
2017-April
Conference Proceeding
In present scenario the energy system face various challenges as the demand for energy is increasing significantly and the resources in terms of fossil fuels are limited, need for renewable resources ...have became very much vital at present. Accurate and reliable solar power forecasting is essential for the legitimate functioning of the power system. Given momentous uncertainties involved in solar power generation due to variation of temperature and irradiance, forecasting provides a unique solution for the uncertainties and variability's in solar data. In this paper a forecasting method has been mentioned that is contingent on a hybrid empirical mode decomposition (EMD) and Extreme Learning Machine (ELM). The non stationary time series is further decomposed into distinct intrinsic mode functions (IMF). A short term forecasting is also carried out in this work to prove the accuracy of the given model. This model is implemented in MATLAB/SCRIPT environment.
This paper presents modelling of a hybrid micro grid, consisting of two DG such as photovoltaic cell and a fuel cell. The micro grid comprises DGs, nonlinear loads, storage devices and operates ...either inter connected or isolated mode from the main distribution grid. Here for the voltage source inverter (VSI) based micro grid the controllers are designed, using various control strategies such as PQ and Fuzzy controllers. Here PQ controller and Mamdani fuzzy control are designed to restore the system during various disturbance conditions like grid connected mode, islanding mode and heavy load switching. The two control techniques have been tested and simulated using MATLAB/SIMULINK package and their results are compared.
In today's world renewable energy sources (like PV, wind etc) has become one of the most promising source of energy for our generation because of its positive effect on environment. One of the main ...aspects that has to be taken care of is the stability of the system when subjected to various fault conditions. This paper presents the design of PI controller for a PV connected micro grid where the stability factor is taken into consideration under various faults like LLLG fault, grid voltage frequency variation. A comparative study was made between the conventional PI controller and the optimized PI controller. The optimization was done with the help of cuckoo search optimization technique and the result was found out with the help of MATLAB/SIMULINK.
This paper presents the application of spider monkey optimization based PI controller to a VSC-HVDC based four machine power system. A PI controller, whose gains are selected by spider Monkey ...optimization technique, is used to minimize the power oscillations and to recover the dynamic stability of VSC-HVDC based four machine power systems. The proposed optimized PI controller is tested under different faults like three phase LLLG fault, Change of torque angle, etc. The comprehensive simulations are carried out in time domain in MATLAB/Simulink environment. The following figures depicts that optimized PI controller is more efficient than conventional PI controller.
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