Hybrid Predictive Models for Accurate Forecasting in PV Systems

The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error

Light Unmanned Aerial Vehicles (UAVs) for cooperative inspection of PV plants

After a fast photovoltaic (PV) expansion in the past decade supported by many governments in Europe, in this postincentive era, one of the most significant open issues in the PV sector is to find appropriate inspection methods to evaluate real PV plant performance and failures. In this context, PV modules are surely the key components affecting the overall system performance; therefore, there is a main concern about the occurrence of any kind of failure in PV modules. This paper aims to propose a novel concept for monitoring PV plants by using light unmanned aerial vehicles (UAVs) or systems (UASs) during their operation and maintenance. The main objectives of this study are to explore and evaluate the use of different UAV technologies and to propose a reliable, cost-effective, and time-saving method for the inspection of PV plants. In this research, different UAVs were employed to inspect a PV array field. For this purpose, some thermal imaging cameras and a visual camera were chosen as monitoring tools to suitably scan PV modules. The first results show that the procedure of utilizing UAV was effective in the detection of different failures of PV modules. Moreover, such a process was much faster and cost effective than traditional methods

Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power

In this paper an artificial neural network for photovoltaic plant energy forecasting is proposed and analyzed in terms of its sensitivity with respect to the input data sets. Furthermore, the accuracy of the method has been studied as a function of the training data sets and error definitions. The analysis is based on experimental activities carried out on a real photovoltaic power plant accompanied by clear sky model. In particular, this paper deals with the hourly energy prediction for all the daylight hours of the following day, based on 48 hours ahead weather forecast. This is very important due to the predictive features requested by smart grid application: renewable energy sources planning, in particular storage system sizing, and market of energy

Adaptive wavelet neural network for wind speed and solar power forecasting for Italian data

Conventional energy sources are nowadays exhausting and that is the reason why renewable energy sources are so important in current situation. In addition renewables are non-pollutant and freely available in nature. Wind and solar power are the fastest growing renewable energy sources for the past few decades, especially according to the 2020 energy strategy in Europe. They are having enough scope in the power market. The main problem with these renewable energy sources is their unpredictability and, in this context, issues like power quality and power system grid stability arise. In order to limit the effects of these issues, power market needs information about power generation at least one day in advance. This problem can be addressed by proper forecasting of Renewable Energy Sources (RES). Forecasting helps to schedule power properly. Adaptive Wavelet Neural Network (AWNN), a technique already assessed in literature for wind speed forecasting, is here applied also to solar power prediction. After forecasting each individual signal, the Mean Absolute Percentage Error (MAPE) is calculated in different time horizons

Performance analysis of grid-connected wind turbines

The development of wind turbines (WT) and the capacities of wind power plants have increased significantly in the last years. Wind power plants (WPP) must provide the power quality required by new regulations and the reliability of the power system that is interconnected to. It is very important to analyze and understand the sources of disturbances that affect the power quality. In this paper is analyzed the performance of three different popular wind generators that are connected to the power system. Based on this analysis was made a comparison for the three wind turbines studied that are: The squirrel-cage induction generator (SCIG), the doubly-fed induction generator (DFIG), and the permanent-magnet synchronous generator (PMSG). The fixed speed system is more simple and reliable, but severely limits the energy production of a wind turbine and power quality. In case of variable speed systems, comparisons shows that generator of similar rating can significantly enhance energy capture as well as power quality. Moreover, performance of their output power leveling is validated by a new method numerically as maximum energy function and leveling function. The performances of these wind turbines and their characteristics are analysed in steady-state. Wind turbines systems are modeled in Matlab/Simulink environment. Simulation results matched well with the theoretical turbines operation

FAULT RIDE-THROUGH CAPABILITY AND DAMPING IMPROVEMENT IN DFIG

Doubly-fed induction generator wind turbine is susceptible to faults and requires crowbar protection. When the crowbar is triggered, the rotor is short circuited over the crowbar impedance. Then, the doubly-fed induction generator operates as a squirrel-cage induction generator that tends to absorb large amount of reactive power from the grid during fault, potentially causing a voltage drop. This paper, therefore, proposes the use of doubly-fed induction generator based lowvoltage-ride-through scheme including crowbar, rotor-side converter, grid-side converter and power system stabilizers. In this way, the transient stability and damping of the electro-mechanical oscillations of a grid-connected doubly-fed induction generator is obtained. The simulation results highlight that the proposed control scheme improves the operation of doubly-fed induction generator during faults. The investigation is realized by comparing the performance of doubly-fed induction generator system with and without the low-voltage-ride-through and damping control schem

Fuel cell characteristic curve approximation using the Bezier curve technique

Accurate modelling of the fuel cell characteristics curve is essential for the simulation analysis, control management, performance evaluation, and fault detection of fuel cell power systems. However, the big challenge in fuel cell modelling is the multi-variable complexity of the characteristic curves. In this paper, we propose the implementation of a computer graphic technique called Bezier curve to approximate the characteristics curves of the fuel cell. Four different case studies are examined as follows: Ballard Systems, Horizon H-12Wstack, NedStackPS6, and 250Wproton exchange membrane fuel cells (PEMFC). The main objective is to minimize the absolute errors between experimental and calculated data by using the control points of the Bernstein-Bezier function and de Casteljau's algorithm. The application of this technique entails subdividing the fuel cell curve to some segments, where each segment is approximated by a Bezier curve so that the approximation error is minimized. Further, the performance and accuracy of the proposed techniques are compared with recent results obtained by different metaheuristic algorithms and analytical methods. The comparison is carried out in terms of various statistical error indicators, such as Individual Absolute Error (IAE), Relative Error (RE), Root Mean Square Error (RMSE), Mean Bias Errors (MBE), and Autocorrelation Function (ACF). The results obtained by the Bezier curve technique show an excellent agreement with experimental data and are more accurate than those obtained by other comparative techniques

Optimal task allocation in wireless sensor networks by means of social network optimization

Wireless Sensor Networks (WSN) have been widely adopted for years, but their role is growing significantly currently with the increase of the importance of the Internet of Things paradigm. Moreover, since the computational capability of small-sized devices is also increasing, WSN are now capable of performing relevant operations. An optimal scheduling of these in-network processes can affect both the total computational time and the energy requirements. Evolutionary optimization techniques can address this problem successfully due to their capability to manage non-linear problems with many design variables. In this paper, an evolutionary algorithm recently developed, named Social Network Optimization (SNO), has been applied to the problem of task allocation in a WSN. The optimization results on two test cases have been analyzed: in the first one, no energy constraints have been added to the optimization, while in the second one, a minimum number of life cycles is imposed

A Selective Ensemble Approach for Accuracy Improvement and Computational Load Reduction in ANN-based PV power forecasting

Day-ahead power forecasting is an effective way to deal with the challenges of increased penetration of photovoltaic power into the electric grid, due to its non-programmable nature. This is significantly beneficial for smart grid and micro-grids application. Machine learning and hybrid approaches are well assessed techniques, able to provide effective forecasting with a data-driven approach based on previous measurements from existing power plants. Ensemble methods can be employed to increase solar power forecasting accuracy, by running several independent forecasting models in parallel. In this paper, a novel selective approach is proposed and assessed, where independently trained neural networks are evaluated in terms of accuracy, in order to properly select a suitable forecasting. Moreover, in order to reduce the associated computational burden, suitably developed new normalization approaches are proposed and evaluated. The considered experimental case study shows that the combination of the proposed procedures is able to increase accuracy and to mitigate the overall computational load, resulting in a simple and lightweight algorithm. Additionally, a comparison with other commonly used techniques has shown that the proposed approach is robust with respect to dataset limited size and discontinuities

Load modeling and scheduling optimization for energy sharing in prosumers network

In the smart home scenario, the deployment of smart meters, smart sensors and home energy management systems (HEMSs) is expected to enable the optimal and dynamic schedule of the domestic energy devices according to different objectives (e.g. self-consumption maximization, peak-period consumption avoidance and emission reduction), while keeping satisfied end-users comfort constraints. However, despite interesting results can be achieved at single-house level, only HEMSs interaction under new business models (e.g. P2P markets) are expected to unleash most of the untapped value. For these reasons, we aim to develop a HEMS model with the right compromise between accuracy and computational burden, suitable to investigate the HEMS performances both at single house level, as in this work, and at community level through a multi-agent simulation framework for future researches. In order to find realist optimal schedule from an end-user perspective, we incorporate detailed sub-models to optimize the trade-off between cost and comfort with a Mixed Integer Linear Problem (MILP) formulation. We consider wet appliances (dish washer, washing machine and tumble dryer), thermal appliances, an energy storage system (ESS) and PV modules. Reported results on the scheduling optimization show the potentiality of the proposed approach