Showing papers in "Iet Renewable Power Generation in 2017"

Using SCADA data for wind turbine condition monitoring – a review

Abstract: The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine supervisory control and data acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine CM is discussed.

Battery-supercapacitor hybrid energy storage system in standalone DC microgrids: areview

Abstract: Global energy challenges have driven the adoption of renewable energy sources. Usually, an intelligent energy and battery management system is deployed to harness the renewable energy sources efficiently, whilst maintaining the reliability and robustness of the power system. In recent years, the battery-supercapacitor based hybrid energy storage system (HESS) has been proposed to mitigate the impact of dynamic power exchanges on battery's lifespan. This study reviews and discusses the technological advancements and developments of battery-supercapacitor based HESS in standalone micro-grid system. The system topology and the energy management and control strategies are compared. The study also discusses the technical complexity and economic sustainability of a standalone micro-grid system. A case study of a standalone photovoltaic-based micro-grid with HESS is presented.

Methods and strategies for overvoltage prevention in low voltage distribution systems with PV

Abstract: The rapid development of photovoltaic (PV) systems in electrical grids brings new challenges in the control and operation of power systems. A considerable share of already installed PV units is small-scale units, usually connected to low-voltage (LV) distribution systems that were not designed to handle a high share of PV power. This study provides an in-depth review of methods and strategies proposed to prevent overvoltage in LV grids with PV and discusses the effectiveness, advantages, and disadvantages of them in detail. On the basis of the mathematical framework presented in this study, the overvoltage caused by high PV penetration is described, solutions to facilitate higher PV penetration are classified, and their effectiveness, advantages, and disadvantages are illustrated. The investigated solutions include the grid reinforcement, electrical energy storage application, reactive power absorption by PV inverters, application of active medium-voltage to LV transformers, active power curtailment, and demand response. Coordination between voltage control units by localised, distributed, and centralised voltage control methods is compared using the voltage sensitivity analysis. On the basis of the analysis, a combination of overvoltage prevention methods and coordination between voltage control units can provide an efficient solution to increase the PV hosting capacity of LV grids.

Power system stability analysis under increasing penetration of photovoltaic power plants with synchronous power controllers

Abstract: The utilisation of renewable sources brings many benefits to electric power systems, but also some challenges such as the impact that renewable power plants employing power electronics have on the grid, which is gaining importance as the penetration of this type of generating stations increases, driven by the construction of large wind or solar photovoltaic (PV) power plants. This study analyses the impact of large-scale PV power plants on a transmission grid for different penetration levels. The analysis considers power plants formed by a number of power converters employing synchronous power controllers (SPCs), that allow them to have a harmonious interaction with the grid, and compares their performance with that of conventional power converter controllers, assuming in both cases that the power plants participate in frequency and voltage regulation. The study addresses both the small-signal stability of the system and its response to large disturbances that alter the active power balance and frequency stability. The results of the analysis show that PV power plants using SPCs are able to limit frequency deviations, improve the oscillation damping, and reduce the stress of other generating units, thus having a beneficial impact on the power system.

Optimal sizing and allocation of battery energy storage systems with wind and solar power DGs in a distribution network for voltage regulation considering the lifespan of batteries

Abstract: The lifespan of a battery in battery energy storage systems (BESSs) is affected by various factors such as the operating temperature of the battery, depth of discharge, and magnitudes of the charging/discharging currents supplied to or drawn from the battery In this study, the optimal location and size of a BESS are found for voltage regulation in a distribution system while increasing the lifespan of the battery Various factors that affect the lifespan of a battery are considered and modelled The problem is formulated as a multi-objective optimisation problem with two-objective functions The first objective function calculates the energy losses in the system, whereas the second objective function represents the total investment cost of the distributed generator (DG) and BESS installations Wind and solar DGs with uncertainties in their output powers are also considered with the BESSs An elitist non-dominated sorting genetic algorithm-II with a utopian point method is used to solve the optimisation problem Furthermore, an IEEE 906 bus European low-voltage test feeder and eight test cases are considered for this study The results show reduced losses and cost, improvement in the voltage profile, and extended lifespan of the batteries

One-day ahead wind speed/power prediction based on polynomial autoregressive model

Abstract: Wind has been one of the popular renewable energy generation methods in the last decades. Foreknowledge of power to be generated from wind is crucial especially for planning and storing the power. It is evident in various experimental data that wind speed time series has non-linear characteristics. It has been reported in the literature that nonlinear prediction methods such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) perform better than linear autoregressive (AR) and AR moving average models. Polynomial AR (PAR) models, despite being non-linear, are simpler to implement when compared with other non-linear AR models due to their linear-in-the-parameters property. In this study, a PAR model is used for one-day ahead wind speed prediction by using the past hourly average wind speed measurements of Cesme and Bandon and performance comparison studies between PAR and ANN-ANFIS models are performed. In addition, wind power data which was published for Global Energy Forecasting Competition 2012 has been used to make power predictions. Despite having lower number of model parameters, PAR models outperform all other models for both of the locations in speed predictions as well as in power predictions when the prediction horizon is longer than 12 h.

Artificial neural network and empirical mode decomposition based imbalance fault diagnosis of wind turbine using TurbSim, FAST and Simulink

Abstract: In this study, an artificial neural network (ANN) and empirical mode decomposition (EMD) based condition monitoring approach of a wind turbine using Simulink, FAST (fatigue, aerodynamics, structures and turbulence) and TurbSim is presented. The complete dynamics of a permanent magnet synchronous generator (PMSG) based wind turbine [i.e. wind turbine generator (WTG)] model is simulated in an amalgamated domain of Simulink, FAST and TurbSim under six distinct conditions, i.e. aerodynamic asymmetry, rotor-furl imbalance, tail-furl imbalance, blade imbalance, nacelle-yaw imbalance and normal operating scenarios. The simulation results in time domain of the PMSG output stator current are decomposed into the intrinsic mode functions using EMD method then RapidMiner-based principal component analysis method is used to select most relevant input variables. An ANN model is then proposed to differentiate the normal operating scenarios from five fault conditions. The analysed results proclaim the effectiveness of the proposed approach to identify the different imbalance faults in WTG. The presented work renders initial results that are helpful for online condition monitoring and health assessment of WTG.

Stochastic multi-objective framework for optimal dynamic planning of interconnected microgrids

Abstract: This paper proposes a new stochastic multi-objective framework for optimal dynamic planning of interconnected microgrids (MGs) under uncertainty from economic, technical, reliability and environmental viewpoints. In the proposed approach, optimal site, size, type, and time of distributed energy resources are determined along with optimal allocation of section switches to partitioning conventional distribution system into a number of interconnected MGs. The uncertainties of the problem are considered using scenario modelling and backward scenario reduction technique is implemented to deal with computational burden. In addition, three different risk averse, risk neutral and risk seeker strategies are defined for distribution network operator. The proposed framework is considered as two unparalleled objective functions which the first objective minimizes the investment cost, operation and maintenance cost, power loss cost and pollutants emission cost and the second objective is defined to minimize energy not supplied in both connected and islanded modes of MGs. Finally, multi objective particle swarm optimization is applied to minimize the proposed bi-objective functions and subsequently fuzzy satisfying method is accomplished to select the best solution proportional to risk based strategies. Efficiency of the proposed framework is validated on 85-bus distribution system and obtained results are presented and discussed.

Energy management for on-grid and off-grid wind/PV and battery hybrid systems

Abstract: Renewable energy systems such as photovoltaic (PV) and wind energy systems are widely designed grid connected or autonomous. This is a problem especially in small powerful system due to the restriction on the inverter markets. Inverters which are utilised in these kinds of energy systems operate on grid or off grid. In this study, a novel power management strategy has been developed by designing a wind-PV hybrid system to operate both as an autonomous system and as a grid-connected system. The inverter used in this study has been designed to operate both on-grid and off-grid. Due to the continuous demand for energy, gel batteries are used in the hybrid system. The designed Power Management Unit performs measurement from various points in the system and in accordance with this measurement; it provides an effective energy transfer to batteries, loads and grid. The designed control unit provided the opportunity to work more efficiently up to 10% rate.

Enhanced utility-scale photovoltaic units with frequency support functions and dynamic grid support for transmission systems

Abstract: This research presents a model of a utility-scale photovoltaic unit (USPVU) enhanced with an embedded hybrid energy storage system (HESS), suitable for stability studies in transmission systems. The main goal of this model is the simultaneous provision of primary frequency control and dynamic grid support. The primary frequency control includes both droop response (achieved by the frequency sensitive mode [FSM] operation) and inertial response (IR). To obtain these grid support functions, the research designed a suitable voltage and frequency (V–f) control, which coordinates the photovoltaic (PV) maximum power point tracking control, HESS converter control, and PV inverter control. Firstly, a midterm assessment of energy requirements in the sized HESS, based on frequency data, validated the energy availability of the enhanced USPVU for primary frequency control, according to new prequalification rules for energy-constrained units. Then, transient stability assessments were performed on a representative transmission system to check the performance of the added FSM and IR in USPVUs with dynamic grid support. The results of the frequency phenomena in the IEEE 39-bus system showed that the enhanced USPVU shared primary frequency control responsibilities with the conventional generation. This was achieved with two criteria to dispatch and commit conventional units by USPVUs.

Fault diagnosis of wind turbine bearing based on variational mode decomposition and Teager energy operator

Abstract: Vibration signal of wind turbine has the non-linear and non-stationary characteristic, thus it is difficult to extract the fault feature In this study, a novel method based on variational mode decomposition (VMD) and Teager energy operator (TEO) is proposed to diagnose the bearing faults of wind turbine First, vibration signal is decomposed into several intrinsic mode function (IMF) components by means of VMD, which is a recently proposed signal decomposition method Then, the most sensitive IMF component is selected according to kurtosis criterion Moreover, TEO is applied to the most sensitive IMF in order to highlight impact signal Finally, spectrum is obtained by applying Fourier transform to Teager energy of the selected IMF, thus extracting the fault feature to diagnose bearing fault The effectiveness of the proposed method for fault diagnosis is validated by simulation and experimental signal analysis results, and comparison studies show its advantage over empirical mode decomposition and conventional spectrum analysis for wind turbine bearing fault diagnosis

Fuzzy unscented transform for uncertainty quantification of correlated wind/PV microgrids: possibilistic–probabilistic power flow based on RBFNNs

Abstract: The probabilistic power flow (PPF) of active distribution networks and microgrids based on the conventional power flow algorithms is almost impossible or at least cumbersome Always, Mont Carlo simulation is a reliable solution However, its computation time is relatively high that makes it unattractive to be a reliable solution for large interconnected power systems This study presents a new method based on fuzzy unscented transform and radial basis function neural networks (RBFNN) for possibilistic-PPF in the microgrids including uncertain loads, correlated wind and solar distributed energy resources and plug-in hybrid electric vehicles When sufficient historical data of the system variables is not available, a probability density function might not be defined, while they must be represented in another way namely possibilistically When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented Hence, a combined solution methodology is needed The proposed method exploits the ability of RBFNN and unscented transform in non-linear mapping with an acceptable level of accuracy, robustness and reliability Simulation results for the proposed PPF algorithm and its comparison with the reported methods for different test power systems reveals its efficiency, accuracy, robustness and authenticity

Integrated wind turbine controller with virtual inertia and primary frequency responses for grid dynamic frequency support

Abstract: An integrated controller of wind turbines with both inertial response and primary frequency regulation (PFR) to provide complete dynamic frequency support for the grid with high wind power penetration is investigated. The wind turbine control governor contains two cross-coupled controllers: pitch controller and maximum power point tracking (MPPT) controller. First, as a precondition for the PFR, a de-loading pitch control scheme is proposed to reserve capacity required for frequency regulation. Then, by optimizing the MPPT scheme, the rapid virtual inertia response is achieved even under de-loading operation condition. Based on the analysis of the steady-state characteristics of wind turbines with frequency droop control, the primary frequency control strategy, which enables the adjustment of frequency droop coefficient, is further proposed through pitch angle changes. Thus, the PFR and inertial response can be both achieved by the proposed de-loading pitch controller and optimized MPPT controller. A three-machine prototype system containing two synchronous generators and a Doubly Fed Induction Generator (DFIG)-based wind turbine with 30% of wind penetration is implemented to validate the proposed integrated control strategies on providing inertial response and subsequent load sharing in the event of frequency change.

Optimal operation of hybrid microgrids for enhancing resiliency considering feasible islanding and survivability

Abstract: Microgrids have the capability to enhance the resiliency of power systems by supplying local loads during emergency situations. However, the disturbance incident and clearance times cannot be predicted precisely. Therefore, this study is focused on enhancing the resiliency of hybrid microgrids considering feasible islanding and survivability of critical loads. The optimisation problem is decomposed into normal and emergency operation problems. In normal operation, unit commitment status of dispatchable generators and schedules of batteries are revised to ensure a feasible islanding following a disturbance event. In emergency operation, the decision between charging of batteries for future dispatch and feeding of lesser critical loads is considered. In addition, a strategy for minimisation of load curtailment during switching of scheduling windows is also considered. These two considerations can mitigate the curtailment of critical loads during the emergency period. Finally, a resiliency index is formulated to evaluate the performance of the proposed strategy during emergency operation. Numerical simulations have demonstrated the effectiveness of the proposed strategy for enhancing the resiliency of hybrid microgrids.

Adaptive reference voltage-based MPPT technique for PV applications

Abstract: The constant voltage (CV) for maximum power point tracking (MPPT) technique is considered one of the most commonly used techniques in the photovoltaic (PV) applications. This study is aimed at proposing an adaptive reference voltage-based MPPT technique (ARV) to improve the performance of the CV technique by making it adaptable to weather conditions. The RV for MPPT is adapted according to the measured radiation and temperature levels. The operating range of the radiation at a given temperature is divided into number of divisions and the corresponding RV is recorded off-line in a truth table. The difference between the reference and measured PV voltages is compensated using proportional-integral controller to generate suitable duty ratio to the boost converter. Performance assessment of the CV technique after being improved covers time response, MPPT efficiency, oscillation and stability. The results present performance improvement by fast time response to reach steady-state value, more stable operation with no oscillation and high MPPT efficiency as compared with the CV technique without the proposed improvement.

Multilevel inverter for interfacing renewable energy sources with low/medium- and high-voltage grids

Abstract: This study presents a new multilevel inverter (MLI) with reduced devices, for low/medium- and high-voltage applications. The proposed MLI is evolved from existing cross-connected source-based multilevel inverter (CCS-MLI), results in reduced switches, driver circuits, diodes, and DC voltage sources when compared with the classical CHB, CCS-MLI, and other MLIs. Owing to reduced device numbers, the complexity, size, cost, and maintenance of the proposed topology are greatly reduced. The detailed analysis and working of the proposed topology is presented along with its comparison with classical, CCS-MLI, and other MLIs. Different algorithms are presented for selecting appropriate magnitudes of DC voltage sources to generate different voltage levels in the output. The proposed MLI is suitable for grid integration of renewable energy sources. The concept is presented through modelling and simulation in MATLAB/Simulation environment and validated through real-time simulator OPAL-RT (OP-4500).

Battery and supercapacitor for photovoltaic energy storage: a fuzzy logic management

Abstract: This study presents an approach of the voltage regulation of DC bus for the photovoltaic energy storage by using a combination of batteries and supercapacitors (SCs). The batteries are used to meet the energy requirements for a relatively long duration, whereas the SCs are used to meet the instantaneous power demand. The energy management strategy is developed to manage the power flows between the storage devices by choosing the optimal operating mode, thereby to ensuring the continuous supply of the load by maintaining the state-of-charge (SoC) of SCs (SoC sc ) and the SoC of the batteries (SoC bat ) at acceptable levels. This energy management strategy is performed by using the fuzzy logic supervisor. The validation results prove the effectiveness of the proposed strategy.

Efficient maximum power point tracking using model predictive control for photovoltaic systems under dynamic weather condition

Abstract: This study presents a high-efficient maximum power point tracking (MPPT) of photovoltaic (PV) systems by means of model-predictive control (MPC) techniques that is applied to a high-gain DC-DC converter. The high variability and stochastic nature of solar energy requires that the MPPT control continuously adjust the power converter operating point in order to track the changing maximum power point; a concept well known in the literature. The main contribution of this study is a model-predictive-based controller with a fixed step that is combined with the traditional incremental conductance (INC) method. This technique improves the speed at which the controller can track rapid changes in solar insolation and results in an increase in the overall efficiency of the PV system. The controller speeds up convergence since MPC predicts error before the switching signal is applied to the high-gain multilevel DC-DC converter and thus is able to choose the next switch event to minimise error between the commanded and actual converter operation. Comparing the proposed technique to the conventional INC method shows substantial improvement in MPPT effectiveness and PV system performance. The performance of the proposed MPC-MPPT is analysed and validated experimentally.

Linear electrical generator topologies for direct-drive marine wave energy conversion- an overview

Abstract: Wave energy is one of the most attractive forms of renewable energy. The reasons include its promising availability, predictability, persistence, and power density. This study focuses on all linear generator designs and technologies which have been used so far in direct-drive wave energy converters (DD-WECs). Currently, linear permanent magnet generators (LPMG) have been proposed as the most advantageous generator system developed for DD-WECs. After a brief description of linear generator based wave energy converters, all proposed state-of-the-art of LPMG topologies available in the literature are discussed and compared in terms of flux path, core type, location of PMs, and etc. In addition, other linear generator technologies such as linear switched reluctance and linear superconducting generators, as an alternative to LPMGs, are reviewed. Finally, based on the surveyed quantitative comparisons performed in previous works, eight major concepts are evaluated in terms of economic and operational aspects.

Superiorities of variational mode decomposition over empirical mode decomposition particularly in time–frequency feature extraction and wind turbine condition monitoring

Abstract: Due to constantly varying wind speed, wind turbine (WT) components often operate at variable speeds in order to capture more energy from wind. As a consequence, WT condition monitoring (CM) signals always contain intra-wave features, which are difficult to extract through performing conventional time–frequency analysis (TFA) because none of which is locally adaptive. So far, only empirical mode decomposition (EMD) and its extension forms can extract intra-wave features. However, the EMD and those EMD-based techniques also suffer a number of defects in TFA (e.g. weak robustness of against noise, unidentified ripples, inefficiency in detecting side-band frequencies etc.). The existence of these issues has significantly limited the extensive application of the EMD family techniques to WT CM. Recently, an alternative TFA method, namely variational mode decomposition (VMD), was proposed to overcome all these issues. The purpose of this study is to verify the superiorities of the VMD over the EMD and investigate its potential application to the future WT CM. Experiment has shown that the VMD outperforms the EMD not only in noise robustness but also in multi-component signal decomposition, side-band detection, and intra-wave feature extraction. Thus, it has potential as a promising technique for WT CM.

Visible defects detection based on UAV-based inspection in large-scale photovoltaic systems

Abstract: The asset assessment and condition monitoring of large-scale photovoltaic (PV) systems spanning over a large geographical area has imposed urgent challenges and demands for novel and efficient inspection paradigm In this study, an automatic UAV-based inspection system is presented and implemented for asset assessment and defect detection for large-scale PV systems Two typical visible defects of PV modules, snail trails and dust shading, are characterised and the defect detection through image processing algorithms based on first order derivative of Gaussian function and feature matching is carried out for the aerial PV module images captured by visible light cameras The functionality of the developed unmanned aerial vehicle (UAV)-based inspection system can be easily extended with more advanced fault detection algorithms and different forms of sensing devices (eg infrared thermal camera) for specialised inspection tasks Such UAV-based imaging can carry out a variety of inspection and condition monitoring tasks in PV systems spanning over a large geographical area in an autonomous or supervised fashion with significantly promoted efficiency in comparison with conventional methods

Stochastic security and risk-constrained scheduling for an autonomous microgrid with demand response and renewable energy resources

Abstract: Increasing penetration of intermittent renewable energy sources and the development of advanced information give rise to questions on how responsive loads can be managed to optimise the use of resources and assets. In this context, demand response as a way for modifying the consumption pattern of customers can be effectively applied to balance the demand and supply in electricity networks. This study presents a novel stochastic model from a microgrid (MG) operator perspective for energy and reserve scheduling considering risk management strategy. It is assumed that the MG operator can procure energy from various sources, including local generating units and demand-side resources to serve the customers. The operator sells electricity to customers under real-time pricing scheme and the customers response to electricity prices by adjusting their loads to reduce consumption costs. The objective is to determine the optimal scheduling with considering risk aversion and system frequency security to maximise the expected profit of operator. To deal with various uncertainties, a risk-constrained two-stage stochastic programming model is proposed where the risk aversion of MG operator is modelled using conditional value at risk method. Extensive numerical results are shown to demonstrate the effectiveness of the proposed framework.

Robust optimisation approach for bidding strategy of renewable generation-based microgrid under demand side management

Abstract: In the restructured electricity market, operator of grid-connected microgrid (MG) tries to supply local load at the lowest cost from alternative energy sources including upstream grid, gas-turbines as local dispatchable units and renewable energy sources (photovoltaic systems and wind-turbines) as well as charge/discharge of energy storage system. In order to purchase power from upstream grid, the bidding curve of MG should be prepared to bid to the market operator. Therefore, this study proposes a robust optimisation approach (ROA) for obtaining optimal bidding strategy of MG. Also, MG operator uses time-of-use rate of demand response program (DRP) to reduce procurement energy cost. For this purpose, ROA is used for uncertainty modelling of upstream grid prices in which the minimum and maximum limits of prices are considered for the uncertainty modelling. The lower and upper bounds of price are consecutively subdivided into sequentially nested subintervals which allow formulating robust mixed-integer linear programming problems. The bidding strategy curves of MG for each time considering DRP are obtained from sufficient data by solving these problems. To show the capability of proposed approach, two cases are studied.

PV plant digital mapping for modules’ defects detection by unmanned aerial vehicles

Abstract: Nowadays plant monitoring and control of renewable energy sources can take advantage of the use of unmanned aerial technologies. This manuscript aims to develop an image post-processing tool for remote aerial images able to help operation and maintenance operators in photovoltaic (PV) defects detection using light unmanned aerial vehicles (UAVs). In particular, PV systems deployed in the field in the last ten years show often critical behaviour with a range of failures able to compromise the performance and energy yield of the power plants, thus they can greatly benefit of such novel technologies and tools. The described procedures are here tested on real plant data, with different kind of sensors, in order to find out potential advantages in fast fault detection tasks, using an automatic system for PV plants’ mapping. The results of this research will be reported in order to provide an understanding of potential impact of image processing techniques based on UAV in the renewable energy sector.

Challenges, advances and future directions in protection of hybrid AC/DC microgrids

Abstract: Hybrid microgrids which consist of AC and DC subgrids interconnected by power electronic interfaces have attracted much attention in recent years. They not only can integrate the main benefits of both AC and DC configurations, but also can reduce the number of converters in connection of distributed generation sources, energy storage systems and loads to AC or DC buses. In this study, the structure of hybrid microgrids is discussed, and then a broad overview of the available protection devices and approaches for AC and DC subgrids is presented. After description, analysis and classification of the existing schemes, some research directions including communication infrastructures, combined control and protection schemes, and promising devices for the realisation of future hybrid AC/DC microgrids are pointed out.

Differential current-based fault protection with adaptive threshold for multiple PV-based DC microgrid

Abstract: A new differential current-based fast fault detection and accurate fault distance calculation is proposed for photovoltaic (PV)-based DC microgrid. A multiterminal direct current (MTDC) distribution network is studied as an adequate solution for present low-voltage utility grid scenario, where local distributed generators (DGs) are incorporated primarily by power electronics based DC-DC converters, DC-AC voltage-source converters (VSCs). PV and diesel generator (as auxiliary source) are considered for cascaded common DC bus, and AC utility bus integration is achieved by VSC unit for the proposed MTDC network. DC microgrid protection is quite significant research focus due to the absence of well-defined standards. Pole-to-pole, pole-to-ground, PV-side DC series and ground arc faults are basically considered as DC distribution network hazards. A discrete model differential current solution is considered to detect, classify and locate the faults by modified cumulative sum average approach. A comprehensive case study is presented with different DC loadings, to deliberate effectiveness of the proposed protection scheme in terms of percentage error and trip time ( Ts ). The result verification is conducted in MATLAB environment as well as TMS320C6713 digital signal processor- based test bench with the proposed multiple DGs based DC microgrid.

Optimisation of grid connected hybrid photovoltaic–wind–battery system using model predictive control design

Abstract: This study explores optimisation of the hybrid power system in the smart grid framework, in conjunction with the model predictive control (MPC) design. This study also creates a strategy that can maximise the use of renewable energy, e.g. photovoltaic, the wind turbine with battery storage and minimise the utilisation of the utility grid for electricity usage in the industry. This is devised by modelling a discrete state-space model of the hybrid power system for a given industry application. The system design is implemented within a real-time electricity pricing environment that is integrated with renewable energy to optimally meet the demand according to a specific performance of the consumer. The emphasis of this approach is on its capacity to supply optimal power to the demand side by selecting the appropriate source; and its robustness against uncertainties. The results show that MPC design for hybrid power system not only optimises the energy flow but also improves the overall process of energy management. It was also observed that the optimal solution minimises the delay cost of energy demand from the utility grid according to a given reference from the consumer for the specified tuning parameter values of the performance index.

Optimal torque control based on effective tracking range for maximum power point tracking of wind turbines under varying wind conditions

Abstract: This study focuses on the development of optimal torque (OT) control, which is a commonly used method for maximum power point tracking (MPPT). Due to the sluggish response of wind turbines with high inertia, conventional OT control was improved to increase MPPT efficiency by dynamically modifying the generator torque versus rotor speed curve. An idea that tracking a local interval of wind speed where the wind energy is primarily distributed rather than the total range of wind speed variation is applied in this study. On this basis, an effective tracking range (ETR) that corresponds to the local interval of wind speed with concentrated wind energy distribution is proposed and an improved OT control based on ETR is developed. In this method, based on a direct relationship between ETR and wind conditions, the torque curve can be quickly optimised so that higher and more stable MPPT efficiency can be achieved under varying wind conditions. Meanwhile, MPPT efficiency enhancement by reducing tracking range without increasing torque discrepancy leads to a low cost of generator torque fluctuation and drive train load. Finally, simulations based on fatigue, aerodynamics, structures, and turbulence (FAST) code and experiments conducted on a wind turbine simulator are presented to verify the proposed method.

Second-order sliding mode control for power optimisation of DFIG-based variable speed wind turbine

Abstract: Power optimisation is quite important for the doubly-fed induction generator (DFIG)-based variable speed wind turbine (VSWT) in the modern renewable power generation system. However, the VSWTs are generally non-linear and uncertain systems. This study proposes a super-twisting second-order sliding mode (SOSM) control scheme to maximise the wind energy capture of a DFIG-based VSWT system, and regulate the stator reactive power to follow the grid requirements. By regulating the generator rotor voltage, the designed SOSM controller makes the wind turbine rotor speed track the optimal speed to maximise the power generation, and controls the rotor current to follow the external reference to regulate the stator reactive power. A quadratic form Lyapunov function is adopted to determine the range of controller parameters and guarantee the finite time stability. Simulation results on a 1.5 MW DFIG-based VSWT demonstrate the effectiveness of the proposed control strategy.

Feed-forward DC-bus control loop applied to a single-phase grid-connected PV system operating with PSO-based MPPT technique and active power-line conditioning

Abstract: This study deals with a double-stage single-phase grid-connected photovoltaic (PV) system operating with an additional feed-forward control loop (FFCL). Owing to the PV array being constantly subjected to abrupt solar irradiance change, the DC-bus voltage varies and can interfere in adequate PV system operation. Therefore, an FFCL is proposed to improve the DC-bus voltage dynamic response, and reduce the settling time and overshoot. The FFCL acts on the generation of the inverter current reference, such that the dynamic behaviour of the current injected into the grid is also improved. Furthermore, the PV system performance is affected by problems associated with mismatching phenomena such as partial shading. This problem can be overcome using the maximum power point tracking (MPPT) technique based on particle swarm optimisation (PSO). The PSO-based MPPT is compared with the conventional perturb-and-observe MPPT technique, in order to highlight its effectiveness. In this study, the PV system also performs active power-line conditioning. Thereby, whereas the step-up DC-DC converter carries out the MPPT, the proper inverter current reference is computed to inject active power into the grid, as well as perform power-line conditioning. The performance and effectiveness of the PV system are evaluated through extensive experimental tests.