In this study, an adaptive noise suppression filter based control algorithm is proposed for integrated voltage and frequency controller (IVFC) of a speed governor-free hydro turbine driven single-phase self-excited induction generator (SEIG). A voltage source converter (VSC) is employed to control the terminal voltage of SEIG with adjustable reactive power. It also mitigates the harmonics injected by non-linear loads in the SEIG system. A resistive dump load with a chopper is connected at dc-bus of VSC. The dump load is controlled to regulate the system frequency at varying loads and mechanical power input to the unregulated micro hydro turbine during seasonal changes. The frequency estimation and phase shifting technique is used for frequency estimation and for generation of quadrature signal of point of common coupling voltage. The proposed IVFC is designed, developed and implemented using a digital signal processor for a two-winding single-phase SEIG of 5 kW rating and test results are presented to demonstrate its performance under steady state and dynamic conditions.

The conventional controller design of the autonomous photovoltaic (PV) energy system feeding a load is usually based on a dual-loop cascade controller structure. The controllers are designed based on the small signal equations of the non-linear system. However, most of the elements comprising the energy system are non-linear in nature such that controllers based on small signal models are not effective in all operability regions. Since the PV-battery-assisted energy system has four inputs and five controlled outputs, it is a non-square mult-input–multi-output system as well. To consider the non-linear behaviour of the system, the concept of feedback linearisation control algorithm is used which provides satisfactory performance, ensures input–output decoupled control on each of the outputs and gives the opportunity to achieve any level of dynamic performance through appropriate locations of the closed-loop eigenvalues and zeros. Different modes of operation of the autonomous, battery-assisted PV system and their controls are investigated and their boundaries of operation are determined. The proposed control algorithm is programmed to operate in all the modes of operation and it is shown that for various load demands, solar irradiation levels and the status of the battery, the control system set forth faithfully follows the reference points. These assertions are validated with computer simulation results.

In a wind power system, the wind turbine captures wind energy and converts it into electric energy through a coupled rotating generator. This renewable energy conversion system usually consists of a wind turbine, rotor, gearbox and mostly a doubly fed induction generator (DFIG). It is a complex non-linear multi-input multi-output system with many uncertain factors. Meanwhile, the dynamics of the system is quite dependent on the wind velocity. Traditional analytical methods are quite difficult to model such a complex system. The recently developed data-driven method can be a suitable modelling technique for such system. Using a large amount of input–output on-line measurement data from the selected months, neural networks and neuro-fuzzy networks are fully utilised to model the DFIG. Detailed analysis and comparisons with the classical system identification techniques are addressed to show the advantages of the data-driven DFIG modelling approach.

High penetration of wind energy in modern power systems led to an increase in the balancing requirements, especially in load-following and unit commitment time frames, which only further compromised the overall reliability and efficiency of electricity supply. One of the possible solutions to address this critical balancing capacity issue is the application of demand response schemes on regular basis for matching electricity consumption and production. This study examines the existing applications of demand response in the given area and proposes an alternative approach based on a proactive dispatch of large industrial consumers by using indirect coordination. The candidate industries suitable for the implementation of the new demand response scheme are sought for and presented. The usefulness and the performance of the proposed arrangement operating as a regular balancing mechanism have been evaluated and demonstrated in several test-case scenarios by applying simplified Monte Carlo simulations.

This study deals with non-linear interaction problems of DFIG (doubly fed induction generator) based wind farm in a stressed power system. The study is performed on an IEEE modified test system using the non-linear analysis based on the modal series (MS) method. As the installed capacity of renewable energy, especially the wind-energy increases, the wind farm connection to the grid should be studied from different viewpoints. The dynamic performance of this composite power system is an important issue and should be investigated carefully especially in stressed conditions and different operating points of system controllers. The non-linear interactions will complicate the dynamic behaviour of power systems. If such interaction exists between wind farm and other system controllers and is quantitatively significant, the stability of wind plant is jeopardised. To verify the analytical results of MS method, the necessary comparisons have been made with the results of the non-linear simulation and approximate linear methods.

This study presents a new technique for detecting rotor electrical faults in wind turbine doubly-fed induction generators (DFIGs), controlled by a stator field-oriented vector control scheme. This is a novel method aimed at detecting and identifying rotor electrical asymmetry faults from within the rotor-side inverter control loop, using the error signal, to provide a future method of generator condition monitoring with enhanced detection sensitivity. Simulation and experimental measurements of the proposed signals were carried out under steady-state operation for both healthy and faulty generator conditions. Stator current and power were also investigated for rotor electrical asymmetry detection and comparison made with rotor-side inverter control signals. An investigation was then performed to define the sensitivity of the proposed monitoring signals to fault severity changes and a comparison made with previous current, power and vibration signal methods. The results confirm that a simple spectrum analysis of the proposed control loop signals gives effective and sensitive DFIG rotor electrical asymmetry detection.

This study investigates the application of the model predictive control (MPC) approach for voltage stability of an isolated hybrid wind–diesel generation system based on reactive power control. This scheme consists of a synchronous generator (SG) for a diesel-generator (DG) system and an induction generator (IG) for a wind energy conversion system. A static voltage automatic regulator (VAR) compensator (SVC) is connected at terminal bus to stabilise load voltage through compensating of reactive power. Two control paths are used to stabilise load bus voltage based on MPC. The first one by controlling the total reactive power of the system that by controlling the SVC firing angle and hence the load voltage. The second control path by controlling the SG excitation voltage and hence the load bus terminal voltage. The MPC is used to determine the optimal control actions including system constraints. To mitigate calculations effort and to reduce numerical problems, especially in large prediction horizon, an exponentially weighted functional MPC (FMPC) is applied. The proposed controller has been tested through step change in load reactive power plus step increase in input wind power. Also, the performance of the system with FMPC was compared with the classical MPC. Moreover, this scheme is tested against the parameters variations.

Sluggish tracking for change in solar irradiations is the main demerit of the perturb and observe (P&O) algorithm because of its fixed perturb. To overcome this, an adaptive tracking algorithm based on Takagi–Sugeno fuzzy implications is proposed in this study. Input to the fuzzy controller is the error between conductance and incremental conductance which is otherwise zero at the maximum power point. The P&O algorithm and the proposed algorithm along with the recently published adaptive incremental conductance algorithm are examined for their performance efficacy on a photovoltaic (PV) generating system with fabricated as well as real irradiation data. As a case study, all the considered algorithms are validated under partial shading conditions also. The effectiveness of the proposed algorithm is verified for the tracking of the maximum power point of a PV system in steady as well as changing irradiations and the conclusions are supported through some experimental validations.

Current technology developments allow the provision of voltage/reactive control of wind turbines. This study focuses on the impact of wind power voltage control on both the transmission network (TNet) and the wind energy harvesting network (HNet). Different possibilities of implementing the control representative of the current situation where wind farms maintain a fixed power factor and future situation where wind farms will provide a proportional voltage control are tested. In addition, optimal controls with a certain objective function such as minimising the HNet losses or optimising the reactive power delivered to the TNet are implemented for comparison purposes. The impact of each implementation of voltage control on the HNet is assessed by means of the value of grid losses, whereas the impact on TNet is measured by the PQ capability curve and the voltage value at the bus that connects the wind energy HNet with the TNet. In addition, the importance of the transformers taps in the control is evaluated and conclusions depending on the topology of the network are obtained thanks to the analysis of two actual Spanish networks.

Quasi-Z source cascaded multilevel inverter (qZS-CMI) is an emerging topology applied to photovoltaic (PV) power system. It can overcome disadvantages of conventional CMI-based PV power system, because of achieving the balanced dc-link voltage through using its boost ability, and saving one-third modules. At present, there was not literature to disclose the modelling and controller design for qZS-CMI-based three-phase grid-tie PV power system. In this study, a control scheme for three-phase qZS-CMI-based grid-tie PV power system is proposed, where distributed maximum power point tracking (MPPT) and constant dc-link peak voltage are achieved for all qZS H-bridge inverter (qZS-HBI)-based PV modules, with grid-injected power in unity power factor. The detailed controller parameter design is demonstrated by using Bode plots and the built models of qZS-HBI-based PV module and whole system. A test bench of 7-level three-phase qZS-CMI-based PV power system is built. Simulation and experimental results validate the proposed control scheme and design method.

In this study, optimal load sharing strategy for a stand-alone hybrid power generation system that consists of wind turbine, diesel generator and battery banks is presented. The diesel generator is used to complement the intermittent output of the wind source whereas the battery is used to compensate for part of the temporary peak demand, which the wind and diesel generator cannot meet thus avoiding oversizing of the diesel generator. To optimise the performance of the system, imperialist competitive algorithm (ICA), ant colony optimisation (ACO) and particle swarm optimisation (PSO) are used to optimal load sharing. These algorithms are used to select the best available energy source so that the system has the best performance.To verify the system performance simulation studies have been carried out using forecasted data (load demand and wind speed). Accordingly, ICA, ACO and PSO are used to train a three-layer feed forward neural network. This trained artificial neural network is applied to short-term wind speed and load demand forecasting on a specific day in the Qazvin. The results show that the proposed control methods can reduce fuel consumption and increase the battery lifetime and battery ability to respond to real-time load turbulences simultaneously.