Smooth Power Research Articles

Impact Analysis of a Battery Energy Storage System Connected in Parallel to a Wind Farm

Increasing wind generation insertion levels on electrical grids through power converters may cause instabilities in the AC grid due to the intermittent wind nature. Integrating a Battery Electric Energy Storage System (BESS) in wind generation can smooth the power injection at the Common Coupling Point (PCC), contributing to the power system voltage and frequency stability. In this article, it is proposed to analyze the operation of a lithium-ion battery technology based 1 MW/1.29 MWh BESS connected in parallel with wind generation with a capacity of 50.4 MW. The main characteristics investigated are power smoothing and power factor correction. Experimental results show that BESS contributes to smoothing the active power and correcting the power factor of wind generation, improving the quality of electrical energy at the PCC.

Do radiative losses determine the characteristic emission of the blazar Mkn 421?

ABSTRACT The radiative loss interpretation for the broken power-law spectra of blazars is often questioned since the difference between the indices does not support this inference. Using the blazar Mkn 421 as a case study, we performed a detailed analysis of its characteristic photon energy where the spectral index changes significantly. We used the observations of the source by Swift–XRT from 2008 to 2019 to identify the characteristic photon energy and the corresponding spectral indices. The spectra in the energy range 0.3–10.0 keV can be well fitted by a log parabola as well as a smooth broken power law. From the smooth broken power-law spectral fit, we show that the spectral indices before and after the characteristic photon energy are strongly anticorrelated. Further, the spectral curvature measured at the characteristic photon energy indicates an anticorrelation with the low-energy spectral index while the high-energy spectral index shows a positive correlation. These findings are at variance with a simple radiative loss interpretation for the characteristic photon energy, and alternative scenarios are thus discussed. Though these scenarios are, in principle, capable of reproducing the correlation results, they deviate significantly from the observed properties.

Evaluation of temporal resolution impact on power fluctuations and self-consumption for a hydrokinetic on grid system using supercapacitors

The power output obtained from the hydrokinetic renewable system fluctuates with changes in weather conditions, which could cause adverse effects on the voltage, frequency and stability of the electrical grid. In this article, an evaluation of power smoothing is performed for a renewable hydrokinetic on grid system using a supercapacitor. The power fluctuations are mitigated and smoothed by the proposed energy control and supplied to the utility grid and household load. Moreover, this paper studies the impact of the temporal resolution of data sampling with respect to the self-consumption of prosumers under technical, economic and environmental parameters using Matlab/Simulink software. The simulation results show that the fluctuations of the hydrokinetic turbine output power can be significantly mitigated, using the supercapacitor based storage system for different temporal resolutions reducing up to 90% of power peaks and power fluctuations produced by the hydrokinetic turbine and the load. Besides, the use of supercapacitors allows increasing the self-consumption of prosumers up to 17.27% for time resolutions of 1 min. Finally, the proposed control reduces up to 0.54 kgCO2/day at cost of energy 0.15 USD/kWh.

Techno-economic analysis of power smoothing solutions for pumping airborne wind energy systems

In pumping airborne wind energy (AWE) systems, the kite is operated in repetitive crosswind patterns, pulling the tether from a winch that drives a generator on the ground. During the reel-out phase of its operation, it produces power, whereas, during the reel-in phase, it consumes a small fraction of the produced power. This leads to an oscillating power profile that requires smoothing before it can be supplied to the electricity grid. This paper proposes three drivetrain concepts as a solution to this power smoothing challenge. The three concepts are based on three different types of storage technologies: electrical, hydraulic and mechanical. Techno–economic models of the drivetrains were developed and a case-study on sizing and costing of the three drivetrain concepts for a MW–scale AWE system was performed. Conclusions were drawn that provide guidance to AWE developers for choosing a suitable drivetrain concept for their systems.

ELD-OSG Control of a Battery-Based Electronic Load Controller for a Small Hydro Energy Conversion System

To provide clean and cheap electricity in remote and hilly areas, where the availability of the utility grid is of concern, small hydro generation possesses a promising solution. This article focuses on the utilization of a small hydro energy conversion (SHEC) system. An enhanced Lyapunov's demodulator (ELD)-based orthogonal signal generator technique is utilized for the extraction of active and reactive components of the load currents in the standalone mode. Moreover, synchronization of the SHEC system is required with the utility grid for smooth power transfer, henceforth the ELD scheme is used for the estimation of frequency whose range complies with the IEEE 1547-2018 std. The system is investigated for both standalone as well as seamless transfer to grid-connected operation and serves the major concerns, i.e., in the standalone mode, it regulates the frequency and terminal voltage to their nominal values at the point of interconnection (PoI). Besides, it mitigates the harmonics injected into the system on account of nonlinear load, which distorts the voltages and currents at the PoI in both modes. Finally, it rectifies the unbalance in the voltages and currents of the hydrogenerator due to the presence of unbalanced loads at the PoI. A comparative analysis of the ELD scheme with other algorithms is provided, which depicts the robustness of the control algorithm. Moreover, comparative performance of estimation of frequency extracted from the ELD approach with other control is also given. A hardware prototype is developed and test results are presented to validate the above-mentioned features. The power quality results depict the total harmonic distortion of the voltages and currents to comply with the IEEE-519 std.

Decentralized Coordination and Stabilization of Hybrid Energy Storage Systems in DC Microgrids

Hybrid energy storage system (HESS) is an attractive solution to compensate power balance issues caused by intermittent renewable generations and pulsed power load in DC microgrids. The purpose of HESS is to ensure optimal usage of heterogeneous storage systems with different characteristics. In this context, power allocation for different energy storage units is a major concern. At the same time, the wide integration of power electronic converters in DC microgrids would possibly cause the constant power load instability issue. This paper proposes a composite model predictive control based decentralized dynamic power sharing strategy for HESS. First, a composite model predictive controller (MPC) is proposed for a system with a single ESS and constant power loads (CPLs). It consists of a baseline MPC for optimized transient performance and a sliding mode observer to estimate system disturbances. Then, a coordinated scheme is developed for HESS by using the proposed composite MPC with a virtual resistance droop controller for the battery system and with a virtual capacitance droop controller for the supercapacitor (SC) system. With the proposed scheme, the battery only supplies smooth power at steady state, while the SC compensates all the fast fluctuations. The proposed scheme achieves a decentralized dynamic power sharing and optimized transient performance under large variation of sources and loads. The proposed approach is verified by simulations and experiments.

An individual pitch control method for floating offshore wind turbine

As the vast deep-water wind resource represents a potential development of a superior resource in comparison to the coast and land, the floating offshore wind turbine (FOWT) represents a viable solution for increasing renewable energy generation. However, above the rated wind speed region, the influence of turbulent wind loads is more severe on the floating offshore wind turbine. Thus, the control system’s primary objective should be to minimize fatigue loads and irregular power output swings to achieve smooth power output. This paper proposes a feedback method of individual pitch control for FOWT that, by utilizing a feedback control strategy, effectively reduces asymmetric aerodynamic loads and platform motion. The effectiveness of the method is verified through simulation verification.

Investigation on Performance of Various Power Control Strategies with Bifilar Coil for Induction Surface Melting Application

In recent years, induction heating applications assisted by electronic power control have been very appealing. For melting applications, induction heating is widely used as it seems to be appropriate and provides higher efficiency, zero pollutants, non-contamination of material, etc. in comparison with conventional heating. The conventional variable frequency control scheme is not sufficient for melting applications because of its high switching loss, low efficiency, and lower heat rate. A superlative control technique is required to control the output power smoothly, for a high heating rate with minimum power loss, and to lower the number of components. In this paper, a capacitorless self-resonating bifilar coil is proposed for induction surface melting applications. The performance of the system in terms of modular losses, heat rate, and efficiency is analyzed for various power methods such as pulse duty cycle control, phase shift control, pulse density modulation control, and asymmetric duty cycle control. An experimental validation is performed for the 1 kW prototype, and the heating rate, efficiency, and modular losses are calculated. The control technique is digitally validated using a PIC16F877A microcontroller with 30 kHz switching frequency. The temperature distribution is analyzed using a FLIR thermal imager. Among the tested methods, pulse density modulation-based control provides smooth and varied power control from 0% to 100% with minimum modular losses. The efficiency of the system is 89% at a rated output power and is greater than 85% for pulse density modulation control with a fast heating rate.

Optimal sizing of hybrid energy storage system considering power smoothing and transient frequency regulation

The increasing integration of renewable energy sources (RESs) poses challenges of active power balance in both the normal operating states and contingencies. The hybrid energy storage system (HESS) consisting of the battery and supercapacitor is flexible, and can provide additional regulation capability. This paper proposes an optimal sizing scheme for the HESS considering power smoothing in steady-state operation and transient frequency regulation after disturbances. First, the output power of RESs is decomposed by the discrete wavelet transform, and the low- and high-frequency fluctuations are separately allocated to the battery and supercapacitor for smoothing. Then, an extended system frequency response model is developed for estimating the required transient frequency regulation capability (TFRC) of the HESS to arrest frequency excursion in contingencies. Furthermore, taking into account the fluctuation smoothing, required TFRC and investment cost, a multi-objective optimization model is established to optimize the size of the HESS. Additionally, the linear weighted method and generalized Benders decomposition are utilized to divide the optimization model into two stages, where a master problem and two subproblems are solved iteratively. A case study is conducted to validate the proposed scheme, showing high cost efficiency, and superior performance in smoothing power fluctuation and improving frequency nadir.

Intelligent Diagnosis Model of Traction Seat of Urban Rail Vehicle Based on Harris Hawks Optimization

Traction seat is an important connecting part of urban rail vehicle, which plays an important role in maintaining smooth running and power transmission of the vehicle body. Timely diagnosis of early failure of traction seat is the key to ensure the safe operation of urban rail vehicles. In order to realize the intelligent diagnosis of traction seat, a multialgorithm fusion scheme based on the Harris Hawk algorithm (HHO) is proposed to realize the fault diagnosis of traction seat. Firstly, the early mechanism of traction seat was studied, and the simulation experiment platform of urban rail vehicle traction seat was built to obtain the vibration data of the early crack traction seat model, so as to facilitate the simulation experiment research. Then, the vibration data of the traction seat were processed by HHO optimized variational mode decomposition (HVMD) to obtain several intrinsic mode functions (IMFs). Secondly, the multiscale permutation entropy (MPE) of each IMF is quantified and its average value is used to construct the energy characteristic vector. Finally, feature vectors are input into the HHO optimized support vector machine (HSVM) model to train a pattern recognizer. Through Python simulation verification, the results show that the model can accurately extract the characteristic information of traction seat and accurately identify the fault type, and the recognition rate reaches 100%.

Performance Analysis of FFBP-LM-ANN Based Hourly GHI Prediction Using Environmental Variables: A Case Study in Chennai

Sun is the sustainable and abundantly available alternative resource on the planet Earth. The uncertain nature of the source caused due to various environmental factors increases the need to quantify the irradiation potential at the targeted location, especially for power smoothing processes, solar-fed electrical applications, and the utility grid. The irradiation measuring devices lead to appropriate maintenance and more expenses and are ineffective under dynamically varying irradiation conditions. Hence, a robust ML-based forecasting technique is sufficient to predict GHI under dynamically varying irradiation profiles caused by various environmental factors. Therefore, this study focuses on short-term (hourly) GHI forecasting using the FFBP-LM-ANN approach. In most of the related articles, the selection of independent variables and designing the network is being a challenge and obstacle in attaining fast and efficient prediction. Such a scenario increases the overall computation complexity and understanding. Hence, this study was intended to use a simple process to crucially select 5 environmental factors and to optimally choose the number of neurons for designing the network for better irradiation prediction. Thus, the network is designed with an input layer of 5 selected environmental variables and one hidden layer with 20 neurons, and hourly GHI in the output layer is performed. The approach is trained using the Levenberg-Marquardt BP algorithm in MATLAB toolbox, with the help of a 4-year dataset received for the rooftop panels of VIT University, Chennai, from NREL. The performance of the model during training and testing was validated and analyzed using 9 performance matrices. As a result, FFBP-LM-ANN satisfactorily predicts hourly GHI for the targeted location based on rRMSE of 7.21%, MAE of 0.042, MBE of 0.000492, R of 0.96, MAPE of 44.4%, MRE of 9.5%, and NSE of 94% obtained under testing process. Moreover, the model has performed much better when compared with 9 related models that exist in literature based on the input variables used, network design, epoch, and RMSE. Subsequently, such a predictor will be adaptable and more suitable for the explicit prediction of hourly GHI for different regions across the world having varying climatic conditions, since the study model is designed for locations facing robust climatic nature. More importantly, the designed model is superior with only environmental variables, which are rarely found in the article, rather than geographical variables, which are predominantly used in most of the related literature.

A novel strategy for implementation of intelligent techniques in solar photovoltaic arrays to improve the performance and various comparison of partial shading mitigating techniques

ABSTRACT This paper proposes a new reconfigure technique to improve efficiency by reconfiguring the photovoltaic array during the different partial shading conditions and comparative analysis with various partial shading mitigating techniques. Normally, the photovoltaic array efficiency is less even if the shading occurs in parts on the PV panels in an array. To extract maximum output from solar PV panels, the different modulation and MPPT techniques are utilized. Due to continuous and discontinuous partial shading, this MPPT technique also fails to perform a uniform power voltage curve. Therefore, to improve the efficiency and performance of the PV array, a prudent solution is needed to dispersing the shading by reconfiguring the solar panel array. Most of the present reconfiguration schemes are quite complex, succumb to erroneous execution, and not suitable for practical implementations. The proposed Intelligent Reconfiguration Technique (IRT) technique is well suited for extension of power ranges and it offers smooth power vs voltage graph there by the algorithm struck up in local maxima can be averted. Also the superiority of the IRT method quantitatively proven in Table 6 by comparing other methods with various shading patterns.

Supercapacitors based energy storage system for mitigating solar photovoltaic output power fluctuations

PurposeNon-linear power–voltage characteristics of solar cell and frequently changing output due to variation in solar irradiance caused by movement of clouds are the major issues need to be considered in photovoltaic (PV) penetration to maintain the power quality of the grid. It is important for a PV module to always function at its maximum available power point to increase the efficiency and to maintain the grid stability. A possible solution to mitigate these generation fluctuations is the use of an electric double-layer capacitor or supercapacitor energy storage device, which is an efficient storage device for power smoothing applications. This study aims to propose a power smoothing control approach to smoothen out the output power variations of a solar PV system using a supercapacitor energy storage device.Design/methodology/approachTo extract the maximum possible power from a PV panel, there are several maximum power points tracking (MPPT) algorithms developed in literature. Fuzzy logic controller-MPPT method is used in this work as it is a very efficient and popular technique which responds quickly under varying ecological conditions, reduced computational complexity and does not depend on any system constraints. Fuzzy logic-based MPPT controller by Boost DC–DC converter is developed for operating the PV panels at available maximum power point. Fuzzy logic-proportional integral (PI) charge controller is implemented by Buck–Boost converter to provide the constant current and suitable voltage for supercapacitor and to achieve better power smoothing. PI charge controller is preferred in this work as it offers better outcomes and is very easy to implement.FindingsSimulation results conclude that the proposed power smoothing control approach can efficiently smooth out the power variations under variable irradiance and temperature situations. To confirm the accurateness of the proposed system, it is validated for poly-crystalline PV module and comparison of results is done by using different case study with and without the use of an energy storage system under change in irradiance condition. The proposed system is developed and examined on MATLAB/Simulink environment.Originality/valueThe performance comparison between PV power output with and without the use of a supercapacitor energy storage device under different Case Studies shows that the improved performance in smoothing of power output was achieved with the use of a supercapacitor energy storage device.

Suitable various-goal energy management system for smart home based on photovoltaic generator and electric vehicles

The energy consumption supervision is required for each smart home to ensure the highest power quality and to enhance the stability of the whole grid. In this paper, a various-goal energy management system is proposed to arrive at a compromise solution for smoothing the smart home power profile, reducing the electricity bill (E.B) and obtaining a free charging of Plug-in Electric Vehicles (PEVs) from home and renewable energy sources (RES). This control has been structured into two hierarchical layers. The first aims to maintain an appropriate balance of the Photovoltaic Generator (PVG) power that is scattered into three essential paths: home, vehicles and grid. The second destinates to coordinate the PEVs charging/discharging modes from and to the smart home. Both optimization layers of the proposed framework are executed using Particle Swarm Optimization (PSO) algorithm. Four scenarios are presented in different seasons with different demand profiles, different electricity prices and illuminations in north Africa, and different PEVs connections states. The simulation results have demonstrated the obtention of smooth home power profiles. As well, the inhabitant can achieve a total daily bill reduction of 26% in autumn, 15.57% in winter, 31.68% in summer and 25% in spring including almost a free charging of PEVs which prove the efficiency of the proposed strategy.

Anti-jerking and traction torque compensation strategy for P3 + hybrid electric vehicle during power upshift

In this study, an anti-jerking and traction torque compensation strategy for P3 + hybrid electric vehicle (HEV) equipped with hybrid automated mechanical transmission (HAMT) during power upshift is proposed. Firstly, to investigate the power upshift characteristics, a powertrain model with three torque ports and flexible drive shaft is established. Secondly, based on the established powertrain model, a hierarchical structure optimization strategy for power upshift is developed. The optimization strategy makes full use of the fast and accurate torque response of the traction motor, aims at simultaneously suppressing the longitudinal jerk and compensating the traction torque, solves the optimal traction motor torque to improve the power upshift performance of P3 + HEV. Finally, a layer-by-layer simulation and a real vehicle test of the power upshift process from first gear to second gear are implemented to verify the effectiveness of the proposed optimization strategy. The verification results show that the proposed strategy can realize smooth and traction torque uninterrupted power upshift process at the same time. Compared with the traditional PI controller the proposed optimization strategy can achieve smaller longitudinal jerk.

Investigating the Complementarity Characteristics of Wind and Solar Power for Load Matching Based on the Typical Load Demand in China

This study explores the potential of renewable power to meet the load demand in China. The complementarity for load matching (LM-complementarity) is defined firstly. Kendall's correlation is employed to quantify the LM-complementarity. Then the complementarity characteristics on the hourly and daily time scales are analyzed. Results reveal that increasing the distance between interconnected power plants has weak improvements on the LM-complementarity in most cases. The LM-complementarity between wind and solar power is superior to that between wind or solar power generated in different regions. The hourly load demand can be effectively met by the LM-complementarity between wind and solar power. The optimal LM-complementarity scenario effectively eliminates the anti-peak regulation feature of wind power and reduces the phase differences between load demand and renewable power generation during a day. However, it is hard to balance renewable power generation and load demand on the daily time scale by the LM-complementarity. Compared with the complementarity for power smoothing defined in previous studies, the instability and peak-to-valley differences of the net-load demand can be effectively reduced by LM-complementarity. Thus the needs for flexibility are effectively reduced when integrating large-scale renewable power into the power system.

Transmission System Electromechanical Stability Analysis with High Penetration of Renewable Generation and Battery Energy Storage System Application

Despite the benefits of wind and solar photovoltaic generation, its stochastic characteristic imposes uncertainties on the electric power system’s transient stability. The dynamics considering large synchronous generators has been studied for many decades, and its behavior is well known. On the other hand, the penetration of renewable sources has reached records, showing that it is still vital to study their impact. The present work proposes computer modeling and simulations for the dynamic analysis of electromechanical stability in a transmission system with significant renewable generation. In general, the literature does not propose solutions to the electromechanical stability analysis, proving that there are gaps to be filled. Therefore, the main work contribution consists of designing and coupling a battery energy storage system to a solar plant to smooth power variations. A significant innovation is the proposition of different scenarios that replicate disturbance situations, where the analyses were carried out using the Brazilian grid code. It was possible to evaluate the robustness of the proposed system and the efficiency of the storage system in mitigating the impacts of renewable generation. Thus, it is possible to achieve high levels of renewable penetration if extensive and rigorous studies are carried out.

Evaluation of power transformer health analysis by internal fault criticalities to prevent premature failure using statistical data analytics approach

Transformers are key players at transmission and distribution level in electrical grid to deliver reliable, efficient, smooth and quality power at consumer end. Power transformers plays a crucial role in the interconnected power systems and is therefore considered as one of the most important and critical assets. Any deterioration occurring due to electrical, thermal, chemical as well as environmental stresses can be identified from health indices and immediate actions can be proposed to avoid any premature failure. This research paper includes the multi criterion based mathematical approach to identify the health indices of power transformers. The proposed approach gives more precise result compared to the conventional methods used for transformer faults diagnosis like gas ratio and traditional experiment based methods to measure health index. The multi criterion considered here are dielectric strength, acidity, breakdown voltage, dissolved gas analysis, furan compounds, dielectric dissipation factor, moisture presence, interfacial tension, winding DC resistance, tan delta etc. It becomes more reliable and proficient to measure the health indices using multi criteria decision making along with piece wise linear equations and Residual Analysis approach for accurate measurement for newly manufactured transformer as well as in service aged transformers. Further, 100 test data set have been added to analyze the performance and implications of health index of 20 completely healthy transformers, 60 partial deformed transformers and 20 complete deformed transformers or on verge of failure. This work gives detailed insights of health status and insulation condition of new transformer, in service transformers or any failure transformer.

New Stability Concept for Primary Controlled Variable Speed Wind Turbines Considering Wind Fluctuations and Power Smoothing

With a further expansion of the wind energy sector, wind turbines (WTs) have an increasing impact on grid stability. Requirements for a grid connection are adapted continuously, especially for frequency support and the fault ride through (FRT) behavior of WTs. Besides the objectives of robust operational stability and maximum energy yield, a smooth of active power is also of interest to reach efficient load to generation balancing of the grid. With stochastic wind fluctuations, power smoothing methods gain importance, but in combination with primary power control, operation points (OPs) with the risk of operational instability occur. In these OPs with speeds below the maximum power point (MPP), a deceleration of the drive train to standstill is possible. While, so far, to ensure a stable operation especially variable droop is preferred for a WT with power controlled generator, a method to guarantee stability on conventional controller principles with constant droop is missing. Therefore, in this article, a control structure with power smoothing, constant droop, and a stabilizing limitation called MPP-limiter is presented, which ensure robust operational stability of the WT without requirements for additional safety margins. Therewith, the provision of the full primary control power is possible. Based on theoretical investigations, parameter studies, individual scenarios, and test bench results, the demand for the MPP-limiter from a stability perspective is shown. Advantages of the power smoothing and its effect on the energy yield are explained, like an increase of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6\,\mathrm{\%}$</tex-math></inline-formula> of total energy in the transition range between partial and full load.

Intelligent Transition Control Approach for Different Operating Modes of Photovoltaic Inverter

The increasing photovoltaic (PV) installations and their integration with the utilities have complexed the operation of the power system network making them vulnerable to various faults and abnormalities. The traditional methods developed to handle this problem are aimed to explore the ability of PV inverter to operate in standalone (SA) mode when there are predictable grid side abnormalities or scheduled maintenances. In this article, a grid condition monitoring based transition control approach is developed using machine learning algorithm and a hybrid control strategy. This article is motivated at handling the intentional and unintentional islanding conditions by operating the PV system in both grid-connected (GC) and SA modes. The switching between the controllers is performed by the central controller ensuring a smooth transition and continuous power delivery to the load. For validating the claims, numerical simulations and experimental analysis are carried out with a 4 kWp GC PV system. The results depicted fast grid condition monitoring under 20 ms and smooth transition without any transients or harmonics.