Iet Generation Transmission & Distribution最新文献

This article introduces innovative protection strategies, including cooperative protection, for power transmission grids amidst a significant shift towards renewable energy sources (RES) such as wind and solar power, as well as inverter-based resources (IBRs). The method employs a global consensus algorithm to achieve cooperative protection efficiently. This scheme leverages consensus protocols to dynamically oversee distance relay decisions, ensuring efficient fault detection and localization. The decentralized nature of the proposed method enhances robustness and security, while its high-speed operation is ensured through non-iterative global consensus algorithms, which provide rapid fault detection and localization crucial for real-time protection. By incorporating virtual leaders and leveraging existing communication infrastructure, the method achieves superior selectivity in identifying faulty lines, enhancing the reliability and stability of power transmission grids with high-RES penetration. Notably, the method does not require learning and training processes, making it adaptable to varying power system topologies without the need for extensive retraining or adaptation periods. The proposed methodology enables simultaneous participation in multiple protection zones by establishing interaction rules between agents. Virtual leaders simplify the selection of protection areas, enhancing scalability and fault localization. Simulation results conducted on the IEEE 39-bus test system validate the effectiveness of the proposed method.

With the development of the Internet of Things (IoT) in power distribution and the advancement of energy information integration technologies, the explosive growth in network data volume caused by massive terminal devices connecting to the power distribution network has become a significant challenge. Multi-terminal collaborative computing is a key approach to addressing issues such as high latency and high energy consumption. In this article, fog computing is introduced into the computing network of the power distribution system, and a cloud-fog-edge collaborative computing architecture for intelligent power distribution networks is proposed. Within this framework, an improved weighted K-means method based on information entropy theory is presented for node partitioning. Subsequently, an improved multi-objective particle swarm optimization algorithm (MWM-MOPSO) is employed to solve the task resource allocation problem. Finally, the effectiveness of the proposed architecture and allocation strategy is validated through simulations on the OPNET and PureEdgeSim platforms. The results demonstrate that, compared to traditional cloud-edge service architectures, the proposed architecture and task offloading scheme achieve better performance in terms of processing latency and energy consumption.

Here, stochastic-gradient-based adaptive control algorithms have been discussed and employed for power quality enhancement in a Photovoltaics (PV) integrated distribution system. Least mean square (LMS), least mean fourth (LMF), sign-error LMS, and $�\epsilon$-normalised LMS ($�\epsilon$-NLMS) have been implemented as control algorithms for the estimation of fundamental load current. The performances of these adaptive algorithms are compared under steady-state and dynamic conditions under the non-linear load conditions in a closed-loop three-phase system. The main aim of implementing these algorithms is reactive power compensation, power quality enhancement, and load balancing in a single-stage three-phase grid-tied PV system. The hysteresis current control (HCC) technique is used to generate switching pulses for the three-phase Distribution Static Power Compensator (DSTATCOM). An MPPT is also employed to ensure maximum power delivery from the solar PV array. PV integrated three-phase single-stage distribution system with adaptive control algorithms is implemented in MATLAB/Simulink environment as well as in experimental environment to achieve the goals per standard IEEE-519.

The correct generator circuit breaker (GCB) dimensioning is essential for the safe and reliable operation of a power plant or generation system. The dimensioning is usually based on standardized calculation methods according to standards (IEC standard 60909-0, IEC/IEEE standard 62271-37-013, IEEE Std C37), often supplemented by selected transient calculations. A non-systematic approach can often be observed here, which does not adequately take into account significant influencing variables or operating states of the generator. This article therefore systematically examines various parameters that influence the short-circuit current components of the generator and are relevant for the dimensioning of the generator circuit-breaker: short-circuit angle, operating point, impedance ratios, phase clearing, switching arc, and fault arc. The results of the current parameters most relevant to the dimensioning of the GCB were then compared for different calculation methods. Special attention was paid to the effect of the switching and fault arc, which were modelled as a constant arc voltage, and its effect on the short-circuit currents is systematically recorded. This work aims to summarize all relevant variables that influence the generator short-circuit current and are relevant for the dimensioning of the GCB and to present the different results based on a short-circuit calculation according to the standard and transient calculation to create a basis for a proper dimensioning of the generator circuit breaker.

The multiterminal LCC-MMC-UHVDC, which employs an LCC on the rectifier side and multiple FHMMCs on the inverter side, has emerged as a cutting-edge technology. Nevertheless, distinct disparities in their protection requirements compared to those of conventional DC grids pose notable challenges in attaining the desired attributes. This paper first derives variation patterns in the time domain of initial waves under different fault conditions. By utilizing the theoretically calculated rate-of-change waveform for the backward current traveling wave in an external fault scenario as a reference, a main protection relay grounded in initial wave process comparison is proposed. This approach capitalizes on the disparity observed in internal faults with the theoretical waveform. To mitigate maloperations stemming from the employment of a non-directional start-up criterion in reverse fault scenarios, subtle noise patterns, mimicking the theoretical waveforms, are infused into the actual waveforms. This approach averts maloperations in reverse faults and obviates the need for added delays associated with directional start-up criteria, thereby enhancing both speed and security. Case studies demonstrate that the proposed protection offers sufficient selectivity and a resistive tolerance of 600 ohms and boasts a speed of 0.2 ms, satisfying the requirements of 800 kV UHVDC systems.

The use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. Here, wavelet transform is applied for denoising, convolutional neural networks (CNN) are used to extract features of the time series, and long short-term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.0148 shows that the wavelet CNN-LSTM is a promising machine-learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real-world applications. The major contribution of this paper is related to improving forecasting using a hybrid method that outperforms other models based on deep learning. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation.

The challenges facing active distribution networks have highlighted the position of the distribution system state estimation (DSSE) process in the distribution management systems as its most important function. Here, regarding the extensive scale of distribution networks and the weaknesses of centralized methods, the decentralized implementation of the DSSE process has received considerable attention. However, predefined network partitioning is supposed in previous works and zone size effects on the performance of the DSSE process have not been assessed. In response, a method for finding the optimal number of network zones and their size is proposed here. For this purpose, initially, an algorithm is used to partition the network into all possible configurations with different sizes. Subsequently, performance metrics affected by zone sizes, such as execution time, accuracy of the DSSE results, and reliability in achieving the results at the control centre, are modelled. Finally, by applying the decentralized DSSE method across all partitioning scenarios and calculating performance metrics, the most efficient and cost-effective partitioning scenario can be identified. The performance of the proposed method is evaluated using the modified 77-bus UK distribution network as an active test case, and the findings are subsequently presented and analysed.

This article proposes a multi-objective and multi-stage low-carbon planning approach for park integrated energy systems (PIES) considering the impacts of random outages from the superior electrical grid. This approach incorporates optimal multi-stage construction sequencing and stepped carbon emission trading to leverage the economic and low-carbon benefits of long-term planning. First, the islanding modes of PIES are described using four random variables: island type, duration, start time, and typical day of occurrence, from which islanding scenarios are generated based on scenario tree. Next, a multi-objective planning model that considers both economics and reliability is constructed, with the objectives of minimizing the total lifecycle planning cost and the expected economic loss during islanding. The improved Normalized Normal Constraint (NNC) method is proposed to solve the multi-objective planning problem. Then, the fuzzy membership function is used to determine the optimal compromise solution, resulting in a planning scheme that balances economic efficiency and supply reliability. Finally, simulations indicate that, at the cost of a slight increase in planning expenses, the proposed model significantly reduces the loss costs under islanding modes compared with single-objective economic-focused planning. Additionally, the improved NNC method can achieve a more uniform Pareto frontier compared with the conventional NNC method.

Protecting DC microgrids (DCMGs) from faults is critical due to the rapid current changes that occur in milliseconds. However, ensuring fast and accurate protection in DCMGs is more challenging than in AC systems. This study proposes a novel protection algorithm using traveling waves (TWs) for fault detection and localization. The high-order synchrosqueezing transform (FSSTH) is applied to precisely identify TWs at the relay location. FSSTH offers a sharp time–frequency representation, enhancing the accuracy and speed of fault detection. This method can accurately detect transient phenomena like TWs in DCMGs, even with noise and variable fault resistance. By using the spectral envelope with FSSTH, ridges in time–frequency representations are extracted, improving fault diagnosis. The approach differentiates external from internal faults and recognizes fault direction by assessing TW polarity. Testing on two different DCMGs showed this algorithm's high efficiency and accuracy, with fault location errors ranging from 1 to 50 meters in low-voltage and 13 to 64 meters in medium-voltage DCMGs, even under challenging conditions like high resistance (up to 500 Ω) and low signal-to-noise ratio (5 dB). These results demonstrate the method's superior accuracy and robustness compared to existing techniques.