Implementing sophisticated voltage controls of wind farms may provide manifold benefits such as reducing losses of the distribution network (DNET) and harvesting network (HNET), increasing the wind hosting capacity of transmission networks (TNETs) and reducing wind curtailments. However, it represents an enormous challenge since wind energy is dispersed increasing the complexity of the required centralised control systems. This study contains a comprehensive state-of-the-art review of the huge research that is available in the literature in order to foster the participation of on-shore wind energy in voltage control. The current situation is described by the actual technological developments of control devices and the regulatory framework of a set of European countries with high wind penetration figures. A classification of key wind voltage control concepts will be proposed in order to understand and organise the available literature references. The state-of-the-art review covers the integration of wind energy through DNETs, smart grids and dedicated HNETs into the TNET, covering research from a steady state and also dynamic perspective. The new requirements of theTransmission system operators tend to suggest that wind farms should have a similar performance compared with conventional plants. Thus, in this study, special attention will be given to the literature review of HNETs since it has been the most promising alternative of integrating wind voltage control into the existing transmission grid hierarchical control schemes. A set of relevant field experiences and international projects will be described, showing some effort to implement academic research into practice.

This study proposes a novel wind turbine generator (WTG) imbalance fault classifier using gene expression programming (GEP). Proposed GEP fault classifier is able to achieve very high classification accuracy with relatively small number of samples. Ours is a first attempt at designing a WTG imbalance fault identifier using GEP for fault segregation. The identifier does not assume prior knowledge of WTG model. Raw current data of permanent magnet synchronous generator stator side are processed through empirical mode decomposition to generate 16 intrinsic mode functions or IMFs. Classifier employs the J48 algorithm to further prune these 16 IMFs to eight most relevant input variables which serve as inputs to the GEP imbalance fault classifier. The authors compare performance of the proposed GEP classifier with other contemporary artificial intelligence (AI) based classifiers such as neural networks and support vector machines. Simulation results and performance comparison against other AI approaches elucidate that the proposed GEP-based identifier could serve as an important tool for WTG fault diagnosis.

This study proposes a new formula for estimating the uncertainty in pitch-controlled wind turbines regardless of the type of wind turbines or the availability of measurement data. The results obtained using a manufacturer's power curve (MPC), which is defined as the representative value of the power performance of wind turbines, may differ from the prediction results of power generation obtained using an actual power curve measured at a real wind farm. Therefore, the uncertainty of the MPC can considerably influence the calculation of power generation. However, the current methods used for uncertainty calculation either use a random constant chosen by the judgment of individual analyzers based on their experience or rely on a limited set of empirical formulas suggested through the analysis of data measured from specific wind turbines. To solve this problem, we suggest a new formula for uncertainty estimation that has general applicability. The suggested formula was validated by comparing its results with the data output measured from a real wind farm. In addition, a comparison of the real-time estimation results of wind power generation and measurement results demonstrates that the proposed formula produces more reliable results than the existing formula.

With increasing the share of wind farms in generation profile, their contribution to frequency regulation has become crucial. This study presents an optimal robust first-order frequency controller in the wind farms, which collectively emulates the inertial response as well as governor droop of synchronous generators. To account for various uncertainties associated with system inertia, damping, and doubly-fed induction generator (DFIG)-based wind turbine's parameters, the robustness of controller is verified through the 16-plant theorem. An analytic method based on the small-signal model of the system is utilised to determine the stability region of the first-order controller. To evaluate the performance of the proposed controller, a DFIG-based wind farm equipped with this controller is added to IEEE 39-bus test system which is updated with wind farms in MATLAB/Simulink environment, and the response of proposed controller is investigated under different conditions. Simulation results verify the robustness as well as the effectiveness of the proposed controller in system frequency regulation.

This study presents a solar photovoltaic distribution static compensator (SPV-DSTATCOM) system feeding a non-linear load connected at point of common coupling. A hybrid variable step size–least mean square–least mean fourth (VSS–LMS–LMF) algorithm is proposed for the control of voltage source converter, which acts as DSTATCOM. A weighing factor is used so that LMS and LMF work simultaneously, and a varying step-size helps in obtaining an adaptable system. The proposed control shows high convergence rate and low value of mean square error. The SPV array uses perturb and observe technique for maximum power extraction. The transition of SPV-DSTATCOM to DSTATCOM and vice-versa is also studied. In SPV-DSTATCOM mode, the SPV feeds power to the load and the grid, resolving the power quality issues of the system, whereas in DSTATCOM mode, the grid feeds power to the load at unity power factor. The system is first simulated in MATLAB environment and then these results are experimentally verified on the developed prototype in the laboratory. The system is studied for both power factor correction and zero voltage regulation modes and under distorted grid conditions. The obtained results adhere to an IEEE-519 standard.

This study highlights the significance of dish-Stirling solar thermal system (DSTS) and high voltage direct current (HVDC) link in the combined automatic load frequency control (ALFC) and automatic voltage regulator (AVR) model of the multi-area thermal-diesel plant. Appropriate generation rate constraints and governor dead band for the thermal plant are considered. A maiden attempt has been made to apply fractional order integral double derivative controller with derivative filter (FOIDDF) as a secondary controller for both ALFC and AVR loops. The performance of the FOIDDF controller is compared with some commonly used classical controllers. The lightning search algorithm is implemented for simultaneous optimisation of the controller parameters. The comparison shows the better performance of FOIDDF than others. The effect of the AVR loop on the ALFC loop is also analysed for the first time in the combined model. The investigation of the effect of DSTS and HVDC links reveals that their inclusion improves the system dynamics. The superiority of the proposed controller has been established for variable insolation of the DSTS. The rigorous sensitivity analysis of the different position and magnitude of disturbance, change in tie-line synchronising coefficient and different condition of the DSTS reflects the robustness of the proposed controller parameters obtained at the nominal condition.

This study proposes a photovoltaic (PV) systems de-loaded margin online calculation method that allows PVs to participate in isolated power system frequency regulation with a proper de-loaded margin. The de-loaded margin determines PVs' frequency regulation capacity. The larger de-loaded margin of PVs provides more reserve capacity for frequency regulation, while more economic benefits are sacrificed correspondingly. Therefore, a tradeoff between PVs' economic benefit and their frequency regulation reserve capacity is necessary. Taking into account these two factors, a dynamic calculation method of PVs optimal de-loaded margin based on the systems real-time operation condition is proposed. Real-time monitoring of the systems operation condition is realised by wide-area measurement system. The calculated de-loaded margin can meet the system frequency regulation requirement with PVs' minimum economic loss. RTDS simulation is conducted in an actual industrial isolated system driven by thermal power and PVs to verify the effectiveness of the proposed method.

This study presents the modelling, control and sensorless speed estimation of two micro-wind turbines deployed by the British Antarctic Survey (BAS) in Antarctica. Mathematical models for the generators attached to an Ampair 100 and Rutland 913 wind turbines and their experimental validation are given. A model for the wind turbines, particularly taking into account the power coefficient versus tip speed ratio relationship, was proposed and successfully evaluated on a wind turbine emulator test rig. This study describes an analogue speed estimator board and a Kalman filter for estimating the shaft speed. These estimators use only DC-side measurements to match the characteristics of the current version of the turbine control board. The wind turbine control and speed estimators were tested on the emulator test rig using real wind data from BAS research bases in Antarctica. Using only DC-side measurements leads to low computation requirements to execute the algorithms in comparison with commonly used schemes that rely on AC measurements. The estimation algorithms are based on the model of a permanent magnet generator connected to a diode rectifier, as they can be used in a wider range of applications including DC–DC converters with maximum power point tracking algorithms.

Moving towards a future power system dominated by renewable energy, it is vital that the siting of spatial-dependent technologies such as wind maximises the power generation while minimising the variability associated with wind power. This study develops a novel methodology for wind farm diversification by identifying pairs and triplets of locations across Western Europe which together form ‘virtual’ wind farms with a better guarantee of a minimum power generation level, providing capacity planners with tools to design a network of connected wind farms working together on a continental scale. These locations were found using hourly wind speed data spanning 10 years by examining time periods of local low wind availability at each grid point and identifying the best complementary wind resource locations. The best links are identified and presented. From an idealised capacity factor (CF) of 0.70 for a single site, the method found the potential for virtual CFs of 0.64 for grid point pairs and 0.68 for grid point triplets. This suggests that this approach can model virtual wind farms with virtual CFs comparable to conventional generation technologies and drastically reduce the amount of time during which farms are producing no power.

In this study, the control and power transfer operation of wound rotor induction generator (WRIG)-based wind energy conversion system (WECS) in a hybrid AC/DC microgrid (DCM), delivering power during islanded and utility-tied conditions are dealt. The system encompasses WRIG, rotor-side converter (RSC) and stator SC (SSC) connected back–back with DCM. AC loads, AC end of SSC, stator of WRIG and a switchable utility frames the AC microgrid. During islanded mode, stator voltage and frequency are regulated with proportional–integral (PI) controllers through RSC. During utility-tied mode, RSC is currently controlled to perform bidirectional slip power transfer. Load compensation is achieved by current-controlled SSC at all conditions. Instantaneous power theory is employed in the current control of RSC and SSC for generating reference currents. On the basis of wind/load conditions, all possible operational modes are formulated. The power transfer operation during occasional conditions such as shorted winding, over loading, low wind, machine stall and no load claims the merit of the setup. A supervisory control algorithm is developed for executing smooth power transfer. The dynamic operation is experimentally analysed to confirm the efficacious working of the WECS.

This study presents an optimisation method to find the best switch set (SWS) topology for reconfiguration of photovoltaic (PV) panels. An approach based on particle swarm optimisation is used to find the optimised topology. In the optimisation, an objective function is defined, in which a minimum number of switches and maximum capability for realising different desired configurations are taken into account. Although the result is a SWS with the capability of reconfiguring PV panels in different series–parallel configurations, the algorithm can be extended for deriving different SWSs aimed to realise other configurations. Some experiments were carried out to validate the proposed approach. The results confirm that the optimised SWS not only can implement different desirable configurations with a minimum number of switches but also able to overcome different abnormal conditions by a suitable switching.

In recent years, many traditional energy consumers are transforming to prosumers with photovoltaic (PV) installations. Thus, energy sharing among prosumers has become a research focus. In this study, the energy sharing problem in a microgrid is formulated as a Stackelberg game. The microgrid operator (MGO) is the leader of the game setting internal buying and selling prices for energy sharing and balances the power mismatch of microgrid by trading energy in day-ahead and real-time market. PV prosumers are the followers of the game deciding their energy sharing profiles in response to internal prices. Each participant of the game makes its best decision to maximise its utility or profit. The Stackelberg equilibrium (SE) is a set of decisions of internal prices and energy sharing profiles, and in SE each participant cannot increase its utility or profit by changing its decision. The existence and uniqueness of the SE have been strictly proved by showing the utility function of MGO is unimodal and has a unique optimal solution. The heuristic algorithm is presented for MGO to achieve the SE in a distributed way, where prosumers get to protect their privacies. Simulation cases have verified the effectiveness and feasibility of the energy sharing strategy.