IET Renewable Power Generation最新文献

This research explores stability challenges in power systems from integrating offshore wind farms (OWFs) with voltage source converter (VSC)-based multi-terminal direct current (MTDC) networks. A novel two-level integrated control (TLIC) framework is proposed to enhance frequency regulation at grid-side VSC (GSVSC) stations. The first level features adaptive inertial control (AIC) and adaptive droop control (ADC). By dynamically adjusting AIC and ADC parameters, wind units (WUs) in maximum power point tracking (MPPT) mode effectively mitigate secondary frequency fall (SFF). WUs are clustered by rotor speeds, enabling staged frequency support for improved responsiveness. The second level uses a communication-independent allocation (CIA) strategy, relying on local frequency measurements in the onshore power system (OPS) to balance power distribution among GSVSC stations. This bolsters OPS frequency stability and minimises SFF during MPPT operations. A robust H∞ controller, designed via loop-shaping, is applied at the wind farm-side VSC (WSVSC), employing multi-criteria decision-making (MCDM) for voltage optimisation. The MTDC DC voltage employs a Master-Slave (MS) configuration to suppress variations under disturbances. MATLAB simulations across scenarios validate the strategy's robustness in damping oscillations from uncertainties.

Ultra-short-term wind power forecasting plays a crucial role in real-time dispatching, frequency regulation, and intraday electricity market transactions. Forecasting accuracy heavily depends on data quality and feature informativeness. However, most existing studies conduct data cleaning offline, with limited attention to real-time data quality during forecasting. Moreover, they often use historical power and NWP data uniformly, neglecting the time-varying importance of input features. To address these issues, this paper proposes an ultra-short-term wind power forecasting method based on dynamic cleaning of streaming data anomalies and adaptive processing of input feature contributions. Firstly, similar samples of the current wind process are retrieved online via time series similarity matching, enabling real-time anomaly detection in streaming data. Secondly, anomalous power sequences are reconstructed using a theoretical restoration model based on wind speed fluctuation identification. Finally, a forecasting architecture with personalised encoding and dynamically fused decoding is designed to enhance prediction accuracy. The proposed method has been successfully applied to a wind-solar-storage power station in Inner Mongolia, supporting both grid dispatching operations and daily maintenance. Compared to baseline methods, it achieves average reductions in forecasting errors of 0.59–9.99 percentage points for RMSE and 0.62–8.49 percentage points for MAE.

The integration of renewable energy sources, particularly wind farms, into modern power systems requires advanced transmission technologies. High voltage direct current (HVDC) systems, especially in multi-terminal configurations (MTDC), are effective for transferring high power to the grid. However, there are concerns about the interaction of HVDC controllers with other devices of the system, which can lead to instability in the power system. Additionally, the complexity of new systems, due to the integration of power electronics and control systems, increases the potential for interaction with the torsional modes of the wind turbine. This paper conducts a nonlinear modal analysis (NLMS) of hybrid MTDC systems connected to wind farms, examining component interactions and their stability on impact. (NLMS is employed as the primary analytical method. The results obtained from this method are compared with those from linear modal analysis and the fourth-order Runge-Kutta (RK4) method.By using the NLMS technique, it reveals insights into complex interactions under various conditions and quantifies how controller parameters affect stability. This research enhances the understanding of dynamics in hybrid HVDC systems and lays the groundwork for future studies and practical applications in resilient power network design and operation.

The microgrid exhibits low inertia levels and small X/R ratios. Consequently, load changes can adversely affect microgrid stability. However, frequency and voltage oscillations can be mitigated through the use of a load frequency control (LFC) system and an automatic voltage regulator (AVR), which serve as secondary control mechanisms. Additionally, the integration of photovoltaic (PV) and wind turbine (WT) in microgrids complicates the performance of LFC and voltage control due to the uncertainties associated with these renewable sources. Therefore, it is essential to employ a suitable controller with optimal parameters. To address this, this paper introduces a novel control technique known as the tilt-proportional-integral-derivative second-order derivative controller (TPIDD²) to concurrently manage the voltage and frequency of microgrids. It also incorporates an intelligent optimisation algorithm integrated with quantum computing, referred to as quantum teaching-learning-based optimisation (QTLBO), to achieve optimal control parameters. The test system consists of a two-area interconnected microgrid, where each area includes various sources such as PV, WT, fuel cell (FC), diesel generator, and battery energy storage system (BESS). The integral of time multiplied by the squared error (ITSE) is utilised as the objective function. To demonstrate the effectiveness of the proposed controller, it is compared with the proportional-integral-derivative (PID) controller. From the ITSE perspective, the proposed controller is 71.96% more effective than the PID controller. Furthermore, the results obtained using QTLBO are contrasted with those from teaching-learning based optimization (TLBO), differential evolution (DE), and RCGA.

In recent years, compound dry-hot events have significantly impacted human society, particularly affecting the source and load sides of the power system. With the increasing penetration of renewable energy, these events pose growing challenges to power supply-demand balances. Therefore, this paper proposes the concept of ‘power drought’ for the first time to quantify the severity of supply-demand imbalances and identify their spatial-temporal evolution under compound dry-hot events. The analysis begins by examining the coupling between meteorological parameters, renewable energy output and load demand under compound dry-hot events. Specifically, the concept of power drought is defined, followed by the formulation of relevant evaluation metrics. Then, a spatial-temporal clustering algorithm and a centroid migration model are applied to analyse the evolution characteristics of power drought events. Finally, the validity and practicality of the proposed method are demonstrated using practical data from a certain region to analyse the evolution of power drought over the past decade. Case studies reveal a south-westward migration of power drought centroids, with 66.49% of grids showing positive correlation between the standardised compound event index and the power drought index.

Currently, reactive power compensation in wind-photovoltaic (PV) hybrid grid-connected systems is typically controlled independently by the wind farm and PV station, lacking a coordination mechanism between them. To address this, we propose a reactive power hierarchical control strategy for wind-PV hybrid systems. Building on an analysis of reactive power source regulation characteristics and reactive power sensitivity, a three-layer reactive power control structure for wind-PV hybrid grid-connected systems is proposed based on the hierarchical control concept. At the system layer, the reactive power compensation requirements for the entire system are determined using the reactive voltage sensitivity of the system collection bus. At the station layer, reactive power allocation tasks for the wind farm and PV station are determined using a hierarchical optimisation control model. At the equipment layer, reactive power allocation tasks for wind turbines, PV inverters, and dynamic reactive power compensation equipment are determined according to the allocation principles governing reactive power sources within the wind farm and PV station. Compared with traditional control strategy, the control strategy in this paper can make full use of the reactive power control capability of the reactive power sources within the system to achieve optimal allocation of the reactive power compensation tasks in the whole system. The average reduction in system network losses reached 8.14%, and the bus voltage at the collection point could be essentially stabilised around 1.0 pu so as to achieve the purpose of improving the stability of the collection bus voltage of the system and point of common coupling (PCC) bus voltage of the station and reducing the system network loss.

In recent years, the use of renewable energy sources has increased significantly. Among these, wind energy stands out as abundant, cost-effective, highly efficient in energy conversion, and environmentally sustainable. This study proposes a hybrid approach based on advanced deep learning techniques for multi-step wind forecasting. The hybrid model integrates enhanced deep learning methods, optimal feature selection techniques, and decomposition transformation models to achieve precise multi-step wind forecasts. Our proposed method incorporates a sequence of robust techniques, including variational mode decomposition for signal discretization, maximum relevance interaction gain for selecting valuable input features, and a predictive model combining convolutional neural networks, gated recurrent units, and bidirectional long short-term memory. This integration leverages the strengths of each model while minimizing their limitations, resulting in improved efficiency and accuracy in forecasting. To evaluate the proposed method, wind power generation data from the Pennsylvania–New Jersey–Maryland (PJM) electricity market and wind speed data from the Favignana Island microgrid were analysed. The results of multi-step wind power forecasting demonstrate the hybrid model's commendable accuracy. For instance, in the PJM electricity market, the average mean absolute percentage error for 2018 ranges from 3.8401% for 1-h-ahead forecasting to 13.8123% for 12-h-ahead forecasting.

This study examined the feasibility of using two primary waste types from a local abattoir for waste management and subsequent biogas production. In the study, wastewater (WW) and rumen content (RC) found at a red meat abattoir were used as substrates during anaerobic digestion (AD). An automated methane potential test system (AMPTS III) was employed to digest the substrates at different doses at 35°C. The raw WW exhibited a soluble chemical oxygen demand (sCOD) of 74 g/L, indicating excessively high levels. Following AD, the maximum COD removal was observed during mono-digestion of RC, achieving a removal rate of 92.6% and a final sCOD of 3.2 g/L. The production of biogas was attributed to high RC loadings, wherein a cumulative biogas production of 1791 NmL/gCOD_removed was produced over 24 days, while biomethane and carbon dioxide production was 491.1 NmL/gCOD_removed and 1300 NmL/gCOD_removed over the same period. The study indicated that the inclusion of RC reduced the rate of pH decline in the digester, suggesting its viability as a material for AD. Typically, mono-digestion of the abattoir WW yields biomethane with a purity of up to 96.96%, while mono-digestion of RC yields high amounts of carbon dioxide.

Seasonal fluctuations and the intermittent nature of photovoltaic (PV) generation create significant challenges for accurate short-term forecasting. This study presents Next Frame Gramian Angular field U-Net (NFGUN), a hybrid deep learning forecasting framework that stands apart from conventional methods by transforming 1D PV time-series data into 2D Gramian Angular Summation Field (GASF) images. Unlike models that rely on direct regression or sky imagery, NFGUN forecasts the next GASF frame using a deep architecture and reconstructs it back into time-series form, effectively capturing nonlinear temporal dynamics. Its uniqueness lies in several key innovations: (1) the integration of Convolutional Long Short-Term Memory 2D (ConvLSTM2D) into a customised U-Net model for better generalisation spatiotemporal features; (2) the incorporation of residual blocks in the bottleneck to preserve deep features while mitigating vanishing gradients and cyclical encoding of time to enrich seasonal patterns; (3) the use of Lanczos interpolation with CIEDE2000 colour difference for high-precision reconstruction from predicted image frames. We evaluate NFGUN against six well-established forecasting methods and measure performance using six accuracy metrics such as MAE, RMSE, and WAPE across all four seasons; NFGUN demonstrates superior performance. Compared to the best-performing benchmark, it achieved improvements in MAE (61.23% winter, 56% spring, 37.45% summer, 59.67% autumn), RMSE (48.34% winter, 64.63% spring, 31.65% summer, 45.83% autumn), and WAPE (49.9% winter, 43.84% spring, 45.83% summer, 48.72% autumn), underscoring its ability to adapt to seasonal variability. These results demonstrate NFGUN's ability to effectively capture complex, seasonal dynamics, making it a robust solution for ultra-short-term PV power forecasting.

The significance of power converters has grown substantially in recent years, driven by rapid advancements in sectors such as renewable energy generation and electric vehicles (EVs). As a result, the need to evaluate the reliability of power electronic devices has become increasingly critical. Research focusing on the degradation of power devices and estimating their remaining useful lifetime has accelerated. Consequently, a comprehensive review of the existing technological research in this domain is essential. This study seeks to provide a valuable reference for the industry by elucidating the core principles of reliability analysis in power converters and comparing various studies conducted in this field. In the context of reliability analysis and remaining lifetime estimation, particular attention is paid to semiconductor switching components, which form the cornerstone of these converters. After detailing the failure modes and mechanisms, the study focuses on the failure data and the measurement techniques employed for its collection. By highlighting the methodologies used in power device modeling and lifetime estimation, this work aims to offer guidance for future research in this area. In this context, the most effective studies conducted in the relevant field in recent years have been examined, evaluated, and presented as a road map for future research.