Several issues need to be considered in the design and control of converters for converter-interfaced distributed generators (DGs). Under fault conditions, the semiconductor devices withstand ratings must not be exceeded. The converter control strategy is also required to facilitate fault ride through (FRT) capability. On the other hand, protection against fault is better served by employing control strategies that allow the converter-interfaced DGs to contribute short duration fault current sufficient to aid the detection of faults. Semiconductor devices protection and FRT capability have the same objective of limiting the magnitude of the fault current. Protection coordination in the complex DG-integrated network is difficult and may result in protection settings that are not optimal or contribute to long relay operating times that may impact FRT support. On this basis, this study proposes the de-coupling of the protection solution from FRT and semiconductor device considerations. This study critically reviews various strategies proposed in the literature for the protection of the DG-integrated distribution system and develops an argument that aims to influence a paradigm shift towards voltage-based protection that may see protection design decoupled from inverter design and control, since fault current contribution may not be required to achieve effective protection.

In this study, a methodology for determining the solar and wind DG (distributed generation) capacity is proposed by using sequential optimisation method considering a seasonal variation of load demand, seasonal solar, and wind variations. The load demand profile is collected from state load dispatch centre; whereas solar data are collected from Indian Institute of Technology Kharagpur and wind data are collected from a weather station. Along with the DGs, the shunt capacitors are also placed to improve the voltage profile and energy loss reduction with different scenarios. For this purpose, minimisation of a multi-objective function is considered. The proposed methodology is applied to a 69-bus distribution network. The results for different scenarios show the substantial reduction in the annual energy loss and improvement of voltage profile. The result also shows that maximum amount of profit is gained with both renewable DGs and shunt capacitors. The impact of load growth on the distribution network with and without renewable DGs and shunt capacitors are compared. The analysis reveals that with integrating the renewable DGs and shunt capacitors in the system, the distribution network can take load growth for few more years without violating the system constraints.

This study analyses the effects of hydrogen concentration enriched biogas on indicated engine cycle work, soot and NO _x emissions of dual-fuel engine. The results show that if advance injection angle of the engine is fixed at the same value as biogas only mode operation, peak value of in-cylinder pressure increases significantly but indicated engine cycle work is only slightly improved with an increase in hydrogen concentration. Optimum advance injection angle decreases as increasing hydrogen concentration in the fuel mixture. At a given engine speed, NO _x emissions decrease with increasing equivalence ratio whereas soot emissions increase almost proportionally with the equivalence ratio. At a given equivalence ratio, NO _x emission increases with the increasing hydrogen concentration in fuel mixture whereas soot emission decreases contrarily. The maximum soot peak value is obtained with slightly rich mixture at equivalence ratio of around 1.1. Lean burn mixture and high hydrogen concentration result in extremely low soot concentration. Soot emission is practically negligible at equivalence ratio of about 0.9 and 20% hydrogen concentration in mixture with biogas. As hydrogen concentration increases, the decrease of optimum advance injection angle reduces simultaneously both soot and NO _x emissions.

Coordinated reactive power regulation is always a critical issue when it comes to a system with a high penetration level of wind energy. This study provides a distributed control scheme for wind farm reactive power regulation, aiming to coordinate the reactive power reference among wind farm clusters. Within the limited communication among neighbouring clusters, fair reactive power generation sharing is achieved. Moreover, the reactive power capability of the wind turbine (WT) is utilised to cooperate with reactive power compensation devices. The characteristics of the collector system are analysed to improve the voltage profile. Instead of averagely assigning the generation order to each WT, the reference is assigned based on voltage sensitivity. Case studies are carried out to validate the performance of the proposed control scheme.

Several forecasting models are combined together to mitigate the uncertainty associated with the solar power generation resource and improve the power generation forecasts. The common ensemble approach in wind and solar power forecasting is the blending of meteorological data from several sources. In this study, the present and the past solar power forecasts, as well as the associated meteorological data, are incorporated into an ensemble learning tool. Since forecasts based on numerical weather prediction systems are more valuable in horizons longer than 6 h, the proposed approach includes the simple persistence model of hour-ahead forecasts along with the different models of day-ahead forecasts so that the combined forecasts become hour-ahead solar power forecasts. In addition, the proposed approach combines the ramp rates of the forecasts to enhance the ensemble learning. Furthermore, the approach improves the ensemble learning by using two loss functions – the first function to minimise errors of the forecasts, and the second to minimise errors of the ramp rates of the forecasts. The performance of the combined forecasts is evaluated over the entire year and compared with other techniques.

The amount of solar energy incidence on a photovoltaic (PV) panel depends on the PV tilt angles with respect to the horizon. It is thus crucial to investigate the optimum tilt angles to maximise the efficiency of PV panels and at the same time to increase the performance of solar energy systems. The objective of this study is to estimate the optimum tilt angle for PV panels in order to collect the maximum solar radiation for the city of Dhahran in Saudi Arabia. A newly developed optimisation algorithm called the vortex search algorithm is used to estimate the solar radiation on the tilted surface. Moreover, one year can be divided into different periods in the proposed approach, and the optimum angle can be obtained for each one of these periods separately. The horizontal solar data (i.e. direct, diffuse and global solar radiation) is used to estimate the optimum tilt angle. The results demonstrate that the solar radiation estimated using the optimum tilt angle is maximised compared with the one estimated on a horizontal surface.

Turbine alarm systems can give useful information to remote technicians on the cause of a fault or stoppage. However, alarms are generally generated at much too high a rate to gain meaningful insight from on their own, so generally require extensive domain knowledge to interpret. By grouping together commonly occurring alarm sequences, the burden of analysis can be reduced. Instead of analysing many individual alarms that occur during a stoppage, the stoppage can be linked to a commonly occurring sequence of alarms. Hence, maintenance technicians can be given information about the shared characteristics or root causes of stoppages where that particular alarm sequence appeared in the past. This research presents a methodology to identify relevant alarms from specific turbine assemblies and group together similar alarm sequences as they appear during stoppages. Batches of sequences associated with 456 different stoppages are created, and features are extracted from these batches representing the order the alarms appeared in. The batches are grouped together using clustering techniques, and evaluated using silhouette analysis and manual inspection. Results show that almost half of all stoppages can be attributed to one of 15 different clusters of alarm sequences.

This study presents an overall sliding mode control scheme for stator power-current control, grid power-current control and dc-link voltage regulation to operate a doubly-fed induction generator (DFIG) based wind energy conversion system. At the generator rotor side, the stator power control is achieved through controlling the rotor currents. The rotor current state model is carried out from the stator and the rotor equations of the generator under the condition of stator voltage alignment. At the grid side, a cascade control loop is applied for the dc-link voltage regulation and the power transfer using grid and dc-link modelling. The structure of the sliding mode control law, combination of compensating, sliding and integral terms, enhances the tracking performance and the robustness to uncertainties. The proposed control strategy is validated using an experimental DFIG wind turbine system and the results are provided to demonstrate the capabilities of the proposed control system in tracking and control under different operating conditions and robustness to uncertainties.

Incipient fault detection of wind turbines is beneficial for making maintenance strategy and avoiding catastrophic result in a wind farm. A deep neural network (DNN)-based approach is proposed to deal with the challenging task for a direct drive wind turbine, involving four steps: a preprocessing method considering operational mechanism is presented to get rid of the outliers in supervisory control and data acquisition (SCADA); the conventional random forest method is used to evaluate the importance of variables related to the target variable; the historical healthy SCADA data excluding outliers is used to train a deep neural network; and the exponentially weighted moving average control chart is adopted to determine the fault threshold. With the online data being input into the trained deep neural network model of a wind turbine with healthy state, the testing error is regarded as the metric of fault alarm of the wind turbine. The proposed approach is successfully applied to the fault detection of the fall off of permanent magnets in a direct drive wind turbine generator.

The net demand of distribution systems of renewable energy shows strong daily and seasonal patterns that may cause a loss of operation and constriction. Battery energy storage systems (BESS) allow peak load shaving and are used to enhance the reliability of distribution systems. In addition to the problem of an unbalanced net demand, there can be significant net demand fluctuation for different times and locations. To address this, mobile BESS (MBESS) can offer advantages over static BESS (SBESS) in operation flexibility, though may require higher engineering costs. In this study, an operation model was proposed to coordinate static and MBESS to improve overall system economic efficiency and reliability. On the basis of this model, a framework was proposed to optimally allocate MBESS/SBESS in a distribution system based on cost–benefit analysis. Using this approach, the optimal operation schedules for MBESS and SBESS can be simultaneously obtained. The proposed optimisation problem uses a new evolution algorithm, a natural aggregation algorithm. A case study on the IEEE test system successfully verified the effectiveness of the proposed approach for the optimal allocation of MBESS/SBESS in distribution systems.

No study in the literature considers both randomness and fuzziness simultaneously, which actually coexist as the penetration of renewable energy in power system increases. In order to handle these two kinds of uncertain features simultaneously, a novel random fuzzy power flow (RFPF) calculation method for a distribution network based on random fuzzy theory is presented here. Firstly, the random fuzzy models of wind and photovoltaic (PV) generation, and loads are set up for the first time according to their features of randomness and fuzziness. Then, a two-fold random fuzzy simulation is conducted to obtain the results of the RFPF calculations; the random simulation stage is based on the 2m + 1 scheme of the point estimate method. Finally, the proposed method is applied to two test systems. The results show that the proposed method is feasible and effective in identifying important areas in the power system affected by distribution generation and loads with these two uncertainties.

This study presents a two-stage competent and efficient approach for optimal operation of wind–pumped-storage-hydro (PSH)–solar–thermal-storage hybrid power plant to get maximum system revenue and profit along with maintaining the grid frequency. The wind speed is predicted for a deregulated market and accordingly, the wind plants are committed to supplying the demand. The operation of PSH, battery and solar power are considered in order to minimise the adverse effect of imbalance cost which comes into the picture due to the mismatch between actual and predicted wind power. The proposed operating strategy for the complex hybrid plant helps to reduce the uncertainty of renewable power sources in an economical manner. Two new energy levels associated with pumped storage, i.e. PE_opt and PE_low and four energy levels associated with the battery, i.e. BE_max, BE_opt, BE_low and BE_min have been considered in this work to show the robustness of the proposed strategy. The proposed approach is implemented and compared using Mi-Power, bat algorithm, particle swarm optimisation algorithm, genetic algorithm and cuckoo search algorithm. Modified IEEE 14-bus system is used to validate the effectiveness of the proposed approach. The bilateral contracts with a double auction bidding model for the competitive power market are also considered for the implementation.