A torque ripple minimising and robust predictive torque control of a doubly fed induction generator (DFIG) is proposed. The proposed method is based on a novel cost function and a robust predictive model. The aim of the proposed cost function is torque ripple reduction beside the torque and flux control. This method predicts the torque and the flux those will be resulted by the feasible voltage vectors for the next step. The best option is chosen and applied based on the proposed cost function. Two expressions are added to the conventional cost function for the torque ripple reduction. These terms minimise the effective value of the torque ripple and the variation of the torque while the main control is not affected. Furthermore, a novel closed-loop prediction model for DFIG is proposed in order to improve the robustness of the method. Simulation results for the conventional finite control set-model predictive control and the proposed methods are presented and compared in order to confirm the effectiveness of this method.

As the penetration of renewable power generation units connected to the grid increases, high power quality and high efficiency have been widespread concerned. The parallel 3LT²Is are necessary to improve the quality and efficiency of applications. The use of common dc-link without isolating transformers will cause the zero-sequence circulating current (ZSCC) problem. This study proposes a finite control set model predictive current control to suppress the ZSCC, balance the neutral-point (NP) voltage as well as control the currents with fast transient response. The cause of the ZSCC generated in the parallel 3LT²I is analysed. The proposed ZSCC suppression technique is achieved through changing the redundant small vectors in the parallel 3LT²Is. Moreover, the coupling problem between the ZSCC and NP balance is solved by the amplitude comparison of NP voltage and ZSCC. As a result, better ZSCC suppression and NP voltage performance can be achieved in different output currents and filter inductance condition. The proposed method has been verified experimentally.

The operating cost reduction of photovoltaic (PV) systems is an important way to increase its economic viability for stakeholders. In this study, an inverter sizing ratio (ISR) analysis is carried out in order to quantify its potential benefit in the context of building-integrated PV systems and PV distributed generation in the central region of Brazil. An algorithm was created to analyse the ISR considering 12 years of measured climate data and data provided by manufacturers of system components. Among three analytical methodologies discussed in this study, the power limitation analysis seems to be the best reference for design adjustments related to a specific location or energy market. By that, a potential reduction over 10% of levelised cost of energy can be achieved in case of varying ISR from the unity value (theoretical) to its new fitted value.

This study presents a new islanding detection technique based on islanding discrimination factor which is derived from the superimposed components of voltages. These voltages are calculated by utilising the acquired voltage signals from the terminal of distributed generators. Various islanding and non-islanding events with varying power mismatches have been simulated on two different widely used networks namely (i) IEEE 34 bus network and (ii) IEC61850-7-420 Micro-Grid model in real time digital simulator (RTDS/RSCAD) environment. The authenticity of the presented scheme has been verified on diversified islanding and non-islanding cases, generated from the above two models. The results indicate that the proposed scheme is able to distinguish islanding situation with non-islanding events accurately. Moreover, it senses islanding condition quickly (within one and half cycle) even with perfect power balance situation and hence, eliminates non-detection zone. In addition, it also provides better stability against nuisance tripping which is initiated due to various types of non-islanding events including reconfiguration of the network. In the end, comparative assessment of the presented scheme with the scheme just published in the literature shows its domination in distinguishing islanding condition with non-islanding events.

In this work, a novel control of single-phase solar photovoltaic (PV) fed unified active power filter (PV-UAPF) is proposed for use in low-voltage distribution system. The shunt voltage source converter (VSC) of PV-UAPF mitigates current power quality issues such as current harmonics, reactive current etc. while the series VSC of PV-UAPF protects the sensitive load against point of common coupling (PCC) voltage disturbances. The shunt VSC control is based on extraction of fundamental load active current using recursive least squares with a variable forgetting factor. The proposed method improves the dynamic response of the system without sacrificing the steady-state performance. The series VSC is controlled using a proportional-resonant controller and it injects voltage in/out phase with PCC voltage during voltage sags/swells, respectively. The PV array which is integrated at the DC link of the PV-UAPF through a reverse blocking diode, provides the load power demand and also supplies remaining power into PCC. The system performance is evaluated using Matlab-Simulink software and is then experimentally verified on a prototype developed in the laboratory under steady-state and dynamic conditions such as irradiation variation, PCC voltage fluctuations and load disturbances to validate the performance of the system.

Conventional relays, such as vector surge relay, frequency relay and rate-of-change-of-frequency relay, are usually employed for islanding detection; however, these conventional relays fail to detect islanding incidents in the presence of small power imbalance inside the islanded system. This study presents an islanding detection approach for synchronous type distributed generation using multiple features extracted from network variables and a support vector machine (SVM) classifier. Features are extracted from a sliding temporal window, whose width is selected so as to achieve the highest detection rate at a fixed false alarm rate. The SVM classifier is trained with linear, polynomial and Gaussian radial basis function kernels, and the parameters of the kernels are tuned to improve the classification performance. The application of the proposed method is illustrated for islanding cases associated with different power imbalance conditions, including small power imbalance conditions associated with the non-detection zone of conventional relays. Furthermore, variation of detection time as a function of power imbalance scenarios, which involve all probable combinations of deficit of active/reactive and excess of active/reactive power imbalance, is assessed in the testing phase. The performance of the proposed approach is evaluated and compared with those of conventional relays in terms of reliability and response time of islanding detection.

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.

This study presents a novel handshaking approach to reduce transformer and line overloading using plug-in electric vehicles (PEVs) connected to a photovoltaic (PV) assisted charging station (CS) via vehicle-to-grid (V2G) control strategy. The discharging behaviour of PEVs on receiving an aggregator command is carried out using a priority index function under two variant of customer command: (i) normal charging; and (ii) fast charging. Moreover, it is carried out to maximise aggregator profit in an optimal fashion by broadcasting to each CS and scheduling their dispatch based on the available power feedback from each CS. The energy command compliance for handshaking is ensured in a sturdy manner using a contingency approach considering the uncertain mobile behaviour of PEVs. Owing to the highly unlikely travelling behaviour of PEVs, a novel distributed consensus-based power restoration between a fleet of CSs is proposed to endure handshaking using average power contribution from the rest of the CS if contingency fails for any CS. The proposed strategy has been tested on a 1 kVA FPGA-based experimental setup to validate the proposed control scheme under different scenarios.

The diagnosis of multi-fault in wind turbine gearbox based on vibration signal processing is considered challenging as the collected measurements from acceleration transducers are often a non-linear mixture of signals induced from an unknown number of sources, i.e. an underdetermined blind source separation (UBSS) problem. In this study, a novel UBSS-based algorithmic solution is proposed to address this technical challenge from two aspects: source number estimation and source signal recovery. The former is realised based on the empirical mode decomposition and singular value decomposition (SVD) joint approach; and for the latter, the observed vibration signals are transformed to the time-frequency domain using short-time Fourier transform to obtain the sparse representation of the signals. The fuzzy C-means clustering and l ₁ norm decomposition are carried out to estimate the mixing matrix and recover the source signals, respectively. The performance of proposed solution is extensively assessed through experiments based on simulation, testbed and realistic wind farm measurements for a range of fault scenarios for both linear and non-linear scenarios. The numerical result clearly confirms the effectiveness of the proposed algorithmic solution for non-linear multi-fault diagnosis of wind turbines.

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 Çeşme 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.

This study presents a hybrid renewable energy system that yields minimum total project cost and maximum reliability. The system is in modular configuration consisting of photovoltaic (PV) array, wind turbine, battery storage, AC load and a dump load. Also, the minimisation of unutilised surplus power is taken into consideration as one of the design objectives. In this study, a new technique named iterative filter selection approach is used in designing the hybrid PV–wind turbine–battery system to obtain the best acceptable solution while considering all the design objectives. The system is then justified by comparing with iterative-Pareto-fuzzy and particle swarm optimisation techniques. The technique is found to be superior in terms of total project cost with satisfaction to the load demand. The method is simulated using MATLAB and the results are presented in the study with proper discussion.

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.

Mutual shading (MS) in photovoltaic (PV) plants occurs when a collector is shadowed by the front row of collectors. The prevention of MS is considered in designing of the PV plants (PVPs). Perforce, this can be ignored in places with low maximum Sun elevation angle during some days and rows spacing limitations. In MS conditions, the reduction in energy and increase in the payback time of PV systems occur. In this study, a new optimal fixed reconfiguration scheme is presented for minimising of the MS effects. In this scheme, the physical locations of modules in a total cross-tied (TCT) PV array are rearranged without altering the electrical interconnections. The presented scheme is able to minimise the MS effects independently from all shading parameters such as Sun position in the sky, latitude of PVP, and PV installation features. For this purpose, the MS pattern is initially determined and its features are explained. Then, an optimal physical arrangement is determined based on the aforementioned pattern. Minimisation of the resistance losses is also considered in the presented reconfiguration. Finally, the comparison of the optimal arrangement with the simple TCT and Sudoku arrangements is given in different conditions.

This study presents a susceptance theory-based sliding mode control (STSMC) algorithm for an improved power quality voltage and frequency control of a single-phase microgrid. The proposed microgrid includes a governor-free small hydro turbine-driven single-phase two winding self-excited induction generator (SEIG), a solar PV array and a battery energy storage system (BESS). The non-linear relationship among magnetising reactance, frequency and speed of SEIG along with random fluctuation in active power output of the solar PV array create the major challenge in frequency and voltage control of such renewable energy-based microgrid. The STSMC algorithm is found suitable to control frequency and voltage of such non-linear and complex system. In this proposed control, the system frequency control in dynamics and steady-state conditions and balance of power among various energy sources and BESS are achieved using the sliding mode control. The STSMC eliminates all possibilities of overshoot and undershoot problem in DC-link voltage of the VSC, which in turn reduces the required size of DC-link capacitor and BESS.

This study deals with various Lyapunov-function-based control (LFBC) schemes proposed for a single-phase grid-connected voltage source inverter with LCL filter. Use of LCL filter causes resonance which may adversely affect the controller's stability. The conventional (CLFBC) scheme employing the inverter-side current guarantees the asymptotic global stability, but it is not able to damp the resonance. As a remedy to the poor resonance damping, the adoption of grid-current and capacitor-voltage feedback schemes have been investigated. Although the former offers a globally asymptotically stable system, it cannot improve the poor resonance damping. However, the CLFBC with capacitor-voltage feedback scheme not only preserves the global stability, but also improves the resonance damping substantially. The analytical equations of the closed-loop poles for each control scheme are provided which can be used to compute the pole positions and the damping ratio needed for a desired response. Furthermore, the effect of changing controller gains on the loci of closed-loop poles is also studied. Simulation and experimental results obtained from 3.3 kW system demonstrate that the CLFBC with capacitor-voltage feedback scheme not only offers a global stability, but also leads to good quality sinusoidal grid current with reasonable total harmonic distortion and fast dynamic response.

This study proposes a voltage-oriented control scheme for four-quadrant grid-tie of a single-phase bidirectional direct current/alternating current (AC) voltage-sourced converter (VSC) to operate smartly at modes of unity power factor (PF) rectifier (UPFR), inverter or static var compensator. Given the possible harmonic-distorted AC grid voltage, the proposed scheme follows or filters the grid voltage to generate the appropriate current command template for minimising the harmonic current while synchronising with the grid. The following template is for the UPFR mode, whereas the filtered template is for the other modes. The scheme proposes a unique band-pass filter (BPF) to proceed with direct component generation for the filtered template. The proposed scheme further unifies the grid-tie inductor current-to-voltage conversion for the following emplate and the quadrature component generation for the filtered template into a differentiator. The control laws under the filtered and following templates are derived. The BPF and differentiator designs are introduced. Simulations and experiments are conducted with a one-cycle control full-bridge VSC to verify the proposed grid-tie control scheme and evaluate its performance.