Wind Generation Research Articles

Techno-economic optimization of a hybrid energy system with limited grid connection in pursuit of net zero carbon emissions for New Zealand

As New Zealand moves towards net zero carbon emissions by 2050, multiple factors must be considered including increased electrical load due to electrification, variability of renewable energy generators, required storage capacity, system economics and limitations on grid transmission capacity. Complex and region-specific interactions between the various design choices involved are likely to require an understanding of a range of optimal and near-optimal designs for proposed micro-grid systems as opposed to a single optimal point. This work develops a novel multiscale optimization algorithm for optimization from a univariate capacity optimization approach to a multivariate one. This enhanced algorithm is applied to a grid-connected hybrid energy system consisting of local wind and solar generation, battery storage, and a limited grid connection for industrial and residential loads. This analysis is repeated for current 2023 and forecasted net zero 2050 grid conditions. Development of local generation allows for a 36.8% reduction of levelized cost of electricity in the 2023 case and a 38.6% reduction in the 2050 case. This results in a projected reduction of 19920 tonnes of CO2/yr. The algorithm and methodology developed are broadly applicable to optimization of next-generation energy grids.

Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances. The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.

Codesign of FDI Attacks Detection, Isolation, and Mitigation for Complex Microgrid Systems: An HBF-NN-Based Approach.

The primary purpose of this article is to design an intelligent false data injection (FDI) attacks detection, isolation, and mitigation scheme for a class of complex microgrid systems with electric vehicles (EVs). First, a networked microgrid with an EV model is well established, which takes load disturbance, wind generation fluctuation, and FDI attacks into account so as to truly reflect the operation process of the complex system. Then, an intelligent hyper basis function neural network (HBF-NN) observer is designed to accurately estimate the state of the microgrids, learn, and reconstruct the possible attack signal online. Subsequently, a novel HBF-NN-based H∞ controller is skillfully designed to mitigate the negative impact of FDI attacks online, so as to ensure the normal operation of the complex systems in an unreliable network environment. Finally, a two-stage integrated intelligent detection and maintenance algorithm is summarized and one simulation is presented to provide tangible evidence of the feasibility and superiority of the proposed FDI attacks detection, isolation, and mitigation methodology.

Switching‐angle‐based hybrid modulation method of full wind speed and low carrier wave ratio for direct‐drive permanent‐magnet wind power generation system

AbstractA switching‐angle‐based hybrid modulation method of full wind speed and low carrier wave ratio for direct‐drive permanent‐magnet wind power generation system is proposed here, which order to improve the control performance of wind turbines in the full wind speed range. The combination manner of the proposed method is based on the harmonic distortion rate of the stator current, a modulation mode with a higher carrier wave ratio is adopted within the maximum switching frequency allowed by the system, the switching angle from optimized synchronous modulation also is introduced. In order to solve the problem of current impact caused by switching different modulation modes, the switching strategy between different modulation modes is developed from the perspective of harmonic current. And the sectors are redivided for the problem of sudden change in carrier wave ratio during switching, which improve the control effect of permanent‐magnet synchronous wind generator when changing the modulations modes. Finally, the feasibility and correctness of the proposed method are verified by theoretical analysis and simulation.

Dynamic Coupling and Cooperative Control for Multi-Paralleled Doubly Fed Induction Generator Wind Farms During Symmetrical Low Voltage Ride-Through in a Weak Grid

Dynamic Coupling and Cooperative Control for Multi-Paralleled Doubly Fed Induction Generator Wind Farms During Symmetrical Low Voltage Ride-Through in a Weak Grid

Analysis of the Problems and Reasons for the Development of Suzhou Hydrogen Energy Industry and Industrial Policies

In recent years, although there have been some policy plans related to hydrogen energy development in China, a complete fiscal policy system has not yet been established. In policies related to hydrogen energy, we cannot keep up with the times. Only some policies are specifically targeted at solar photovoltaic and wind power generation, and some policies are even temporary. These fiscal policies are severely lagging and lack uniformity, which not only weakens the overall effect of the money supply, but also to some extent hinders the healthy and rapid development of the hydrogen energy industry. In addition, the national development plan, including national and local plans, cannot be well connected, resulting in some policies not forming strong support for hydrogen energy. In these industrial plans, production capacity targets account for the majority, but there is a lack of refinement of utilization targets and little mention of the development balance of the industrial chain.

A Novel Adaptive Third-Order Continuous Super-Twisting Controller of a Five Phase Permanent Magnet Synchronous Wind Generator

The aim of this research is to enhance the control of a Wind Energy Generating System (WEGS) incorporating a Five-phase Permanent Magnet Synchronous Generator (5 ph-PMSG) and addressing operational challenges in the presence of disturbances. The commonly employed Second-Order Super-Twisting Controller (SOSTC), with constant gains, has limitations, including uncertainties, significant transient state errors, and chattering effects, hindering its practical applicability. To overcome these drawbacks, the study proposes the application of an Adaptive Third-Order Continuous Super-Twisting Control (ATOCSTC). This novel approach integrates a third-order sliding mode surface, a smoothly continuous switching control term, and an adaptation law. Unlike the conventional SOSTC using discontinuous “sign” functions, ATOCSTC dynamically adjusts gains using tangent hyperbolic functions, providing a continuous and effective control mechanism. Comparative analysis with the conventional SOSTC under various disturbances such as wind speed fluctuations, parameter variations, and Grid Voltage Sag (GVS), reveals significant improvements with ATOCSTC. Notably, ATOCSTC leads to a 75% reduction in power ripples for grid active power and a 40.38% reduction for grid reactive power. These findings underscore the superior performances obtained with the proposed ATOCSTC in WEGS.

Investigating the effectiveness of a mobile wind turbine generating electricity from vehicle air movement

This study presents an evaluation of the effectiveness of a portable wind generator designed to harness wind flow generated by moving vehicles for electricity production. With a growing emphasis on renewable energy sources and the need for sustainable power solutions, innovative approaches such as utilizing vehicle-induced wind currents have gained attention. The portable wind generator under examination is engineered to capture airflow from passing vehicles and convert it into electrical energy. Through a combination of field experiments and theoretical analysis, the performance and efficiency of the device were assessed under various environmental and traffic conditions. Factors such as wind speed, direction, and vehicle velocity were considered in the evaluation process. The results indicate promising potential for the portable wind generator as a supplementary power source, particularly in environments with high vehicular traffic. However, challenges such as variability in wind patterns and optimal positioning of the generator remain areas for further investigation and refinement. Overall, this study contributes to the understanding of utilizing unconventional wind resources and provides insights into the practical implementation of mobile wind energy technology for sustainable electricity generation.

LSTM-Autoencoder Based Anomaly Detection Using Vibration Data of Wind Turbines.

The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was performed using the vibration signal of a wind power generator to achieve an outlier detection performance of 97%. We analyzed the vibration data through wavelet packet conversion and identified a specific frequency band that showed a large difference between the normal and abnormal data. To emphasize these specific frequency bands, high-pass filters were applied to maximize the difference. Subsequently, the dimensions of the data were reduced through principal component analysis, giving unique characteristics to the data preprocessing process. Normal data collected from a wind farm located in northern Sweden was first preprocessed and trained using a long short-term memory (LSTM) autoencoder to perform outlier detection. The LSTM Autoencoder is a model specialized for time-series data that learns the patterns of normal data and detects other data as outliers. Therefore, we propose a method for outlier detection through data preprocessing and unsupervised learning, utilizing the vibration signals from wind generators. This will facilitate the quick and accurate detection of wind power generator failures and provide alternatives to the problem of energy depletion.

A CCP-based distributed cooperative operation strategy for multi-agent energy systems integrated with wind, solar, and buildings

To explore the bidirectional interaction between renewable energy and buildings in multi-agent energy systems, this paper proposes a distributed cooperative operation strategy for multi-agent energy systems integrated with wind, solar, and buildings based on chance-constrained programming (CCP). First, the multi-agent energy system integrated with wind, solar, and buildings is comprehensively modeled with detailed electric and thermal characteristics for flexibility enhancement. Then for maximizing the profits of the cooperative energy system and each engaged agent, a Nash bargaining model is presented and is divided into two subproblems: the coalition income and the power payment. To preserve the privacy of agents, the adaptive alternating direction method of multipliers (ADMM) is exploited to solve both subproblems. Meanwhile, the CCP method is applied to address diverse uncertainties from wind and solar power generation as well as outdoor temperature. Finally, the effectiveness of the proposed strategy is validated. The simulation results show that, besides the privacy of information among all agents being well preserved, our strategy enhances the profits not only for the energy system but also for all engaged agents.

Dynamic Demand Modeling Incorporating Renewable Energy Sources Using a Population-Based Optimization Method

Due to the inclusion of distributed generation (DG) in microgrids (MGs), the accelerated growth in demand, and environmental concerns, suitable management and operational strategies are imperative. The utilization of wind and solar energy has rapidly increased in MGs. However, due to the uncertainties these systems present, accurately predicting energy generation remains challenging. This necessitates modeling the system’s random variables (such as renewable resource output and possibly load demand) using appropriate and feasible methods. The primary objective of this article is to determine the optimal setpoints for renewable energy sources (RES) and all elements involved in the MG, minimizing the total operation cost. The system comprises wind turbines (WT), photovoltaic panels (PV), energy storage systems (ESS), and electric vehicles (EVs). Weibull distribution and the Hottel and Liu Jordan equations are employed to determine the potential available capacity of wind and solar energy generation, respectively. ESS is utilized to enhance MG performance. For optimal management, a comprehensive mathematical model with practical constraints for each MG element is extracted. An efficient Population-Based Incremental Learning (PBIL) metaheuristic method is proposed to solve the optimization objective in an MG, demonstrating that this energy management system optimizes and effectively coordinates DG and ESS energy generation considering economic considerations. Finally, PBIL is compared with a commonly used model, Particle Swarm Optimization (PSO), across various scenarios, analyzing and evaluating their outcomes, showcasing a reduction in operation costs.

Green energy as a key element of the implementation of the concept of distributed generation

Purpose. The purpose of this article is to study the role of "Green energy" in the concept of generation distribution, to analyze the contribution of renewable sources to the stability of energy. The main tasks include improving models of electric photo panels and wind generators to achieve environmental and economic efficiency. Methodology. Mathematical modeling of photovoltaic panels and wind generators, analysis of the effect of the precision factor and the efficiency factor of the inverter on power generation. A comparative analysis of the obtained results with real data is used to validate the models. The aerodynamic characteristics of the wind generator and the influence on the output power are studied. An optimized model for forecasting the efficiency of hybrid systems using green technologies is being developed. Findings. The obtained research results reveal the key contribution of "Green Energy" to the concept of distributed generation. The methods of mathematical model panels and the use of photovoltaic generators, together with the application of the current coefficient and taking into account the efficiency of the inverter, made it possible to increase the accuracy of forecasting electricity generation. The developed model takes into account the aerodynamic characteristics of the wind generator, emphasizing the realized possibility of exceeding the nominal power in accordance with the real characteristics. The obtained results are compared with real data for validation and confirmation of the effectiveness of the system. Originality. It consists in improving the model of photovoltaic panels, taking into account the influence of the inverter and the aerodynamic features of the wind generator. The work found that the largest generation of wind generators on earth is from October to February, indicating the advantage of combining solar and wind energy. Practical value. It consists in optimizing the forecasting of electricity generation in hybrid systems, which determined the increase in accuracy and adaptability to real conditions. Your study represents the potential for the development of "Green Energy" in communities with a large area and medium capacity.

Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances. The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.

Complementary assessment and design optimization of a hybrid renewable energy system integrated with open-loop pumped hydro energy storage

The intermittent nature of renewable energy resources is a major obstacle in utilizing such resources for power generation despite their abundance. In this paper, a dynamic analysis of an integrated system based on photovoltaic (PV) panels and wind turbines coupled with pumped hydro energy storage is presented. When the power generation exceeds the power demand, surplus electricity is used to pump the water to the upper reservoir. The natural inflow of the river obviates the necessity for oversized reservoirs and saves costs. A recently introduced multi-objective equilibrium optimizer algorithm is developed to find the optimal size of the integrated hybrid energy system. The objectives are to minimize the levelized cost of electricity and maximize the reliability of the proposed system. The results show that the correlation between solar and wind power generation in the selected location is mildly strong with a value of 0.3. The total energy generation from hydro, solar, and wind resources are 77,508.7 kWh, 103,323 kWh, and 47,375.4 kWh, respectively. Comparing the results with other algorithms indicates that the equilibrium optimizer has a better performance in finding the optimal design.

Wind‐assisted microgrid grid code compliance employing a hybrid Particle swarm optimization‐Artificial hummingbird algorithm optimizer‐tuned STATCOM

AbstractThe importance of resolving stability concerns in weak AC grid‐connected doubly fed induction generator (DFIG) wind energy systems during low‐voltage ride‐through (LVRT) events cannot be ignored, given the increasing popularity of wind power‐based microgrids. Furthermore, the emergence of generation loss and postfault oscillation within a microgrid (MG) due to grid faults has also become a significant concern. The static synchronous compensator (STATCOM) under consideration in this study is tuned using particle swarm optimization (PSO), the artificial hummingbird algorithm (AHA), and a hybrid approach incorporating both PSO and AHA. Faults of both a symmetrical and an asymmetrical nature have occurred on the power grid side. The proposed hybrid PSO‐AHA‐tuned STATCOM strategy aims to improve LVRT, minimize power generation loss during faults, and reduce oscillations after a fault by controlling the flow of reactive power between point of common coupling (PCC) and MG. The MATLAB simulation environment was used to simulate the 16 MW MG test system. The performance of the PSO‐AHA‐tuned STATCOM was assessed by comparing results with those from conventional STATCOM, PSO, and AHA optimizer‐tuned STATCOM in four fault situations. A comparison of the results shows that the proposed strategy performed better than other approaches mentioned in this paper and achieved the desired objectives.

Geospatial optimisation of wind and solar energy generation: A framework for optimised resource allocation

Wind and solar power generation profiles exhibit a high degree of power output variability and uncertainty compared to conventional fossil fuel generation plants. However, it is generally accepted that this variability and uncertainty can be mitigated by geographic siting and dispersion of the renewable energy plants, particularly with the view to leverage aggregation effects. In this context, this paper proposes a framework for the geospatial optimisation of wind and solar PV generation for scenarios with a high penetration of wind and solar renewable energy. The proposed optimisation framework represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, the wind and solar resources are categorised in terms of the statistical properties of the associated spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time of Use periods. It is thereby recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time of Use periods are more important than others. The proposed framework is implemented for a number of baseline case studies and optimisation case studies, using wind and solar resource data sets covering South Africa. Overall, it is concluded that this approach can leverage geospatial site optimisation to substantially reduce the risks associated with high residual load events. Furthermore, the proposed framework is highly flexible in that the formulation of the minimum and maximum allocation constraints allows application for real-world scenarios whereby stringent capacity allocation constraints can be applied.

An unsupervised adversarial autoencoder for cyber attack detection in power distribution grids

Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations and data labels. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other data-driven methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids.

A Modified Reduced-Order Generalized Integrator–Frequency-Locked Loop-Based Sensorless Vector Control Scheme Including the Maximum Power Point Tracking Algorithm for Grid-Connected Squirrel-Cage Induction Generator Wind Turbine Systems

A Modified Reduced-Order Generalized Integrator–Frequency-Locked Loop-Based Sensorless Vector Control Scheme Including the Maximum Power Point Tracking Algorithm for Grid-Connected Squirrel-Cage Induction Generator Wind Turbine Systems

Characteristics and mechanism analysis of the clean evolution of China's power generation structure

The power industry in China is the foremost emitter of carbon, necessitating a clean transition in its power generation segment for successful decarbonization. Identifying and quantitative analysis the critical factors that shape the transition and their operational mechanisms will help achieve a secure low-carbon transformation of the energy system. Nonetheless, the body of extensive research in this particular field remains notably limited. To bridge this gap, this paper initiates by developing a comprehensive mechanism framework (REEP) for the transition towards cleaner power generation including Resource endowment (abbreviated as R), Electricity market (abbreviated as E), Economic development (abbreviated as E) and Policy implementation (abbreviated as P) four fields. This framework is based on monthly power generation data derived from four categories of power generation sources at the provincial level in China, spanning the years 2014–2019. It dynamically and quantitative elucidates the multifaceted impacts of various influencing factors on the evolution of the power generation structure. Based on the REEP framework, we observe the power generation structure evolution trends. The study's findings underscore the significance of resource endowment as a paramount internal factor influencing the clean evolution of the power generation structure, while economic development emerges as a pivotal external driving force propelling its transformation. Despite wind and solar power generation accounting for a relatively small proportion at the end of the 14th Five Year Plan period, it is imperative for China to continue promoting the extensive development of renewable energy. While we should coordinate national power transmission and distribution on the foundation of incentivizing technological innovation in emission reduction and the development of energy storage technology. This approach is essential to comprehensively advance the low-carbon and stable development of the energy system, ultimately facilitating the realization of the dual-carbon goal.

An experimental study on the influence of wind-wave-current coupling effect on the global performance of a 12 MW semi-submersible floating wind turbine

Investigating the coupling effect between different marine environmental conditions is essential to understand the dynamic response of wind turbines. This paper presents the study findings from the model tests conducted in a wave basin, exploring the coupling effect of wind, waves, and currents on the global performance of a 1:70 scale model 12 MW semi-submersible floating wind turbine (FWT). A multi-blade large-scale wind generation system with a rectifier network was improved and fabricated to provide a reliable wind field for the experiment. The stiffness-matched and geometry-matched tower models were designed and fabricated, achieving an accurate simulation of the tower's flexibility and windward areas and their impact on the global dynamic response. Following the calibration of marine environmental conditions and the characterization of the model FWT system, the study focused on the operational and extreme responses of the FWT concept. The aerodynamic damping effect of wind in pitch and surge motions is remarkable, especially in pitch motion, primarily manifesting by reducing the standard deviation of motions. Mooring load responses are predominantly induced by the hydrodynamic effect, with the wave-frequency response gradually becoming dominant as wave parameters increase. Tower-top load responses are primarily induced by the hydrodynamic effect of waves and the aerodynamic load effect of wind. Furthermore, the phenomenon where the action of current strengthens the aerodynamic damping effect of wind is observed. The coupling effect between wind and current amplifies surge motion while diminishing pitch motion, effectively reducing the tower-top and tower-base load responses.