Electrical Engineering最新文献

This contribution details the effective and accurate determination of the time-domain harmonic state estimation (TDHSE) of power networks interconnecting wind generation sources. The TDHSE method obtains the state variables of power networks and the system inputs. The algorithm uses a limited number of measuring devices, which can be contaminated with noise and/or gross errors. The wind generation source model represents the dynamic operation of a wind energy conversion system. It is based on a type-4 wind turbine and a direct-drive permanent magnet synchronous generator with a full scale back-to-back power converter. The wind generation source assumes wind fluctuations and changes in the angular speed control drive to estimate its dynamic behaviour and the effect on the power network. Case studies are considered for the analysis of a three-phase power network, i.e. with harmonic injections, with a wind generation source and with a wind farm. The TDHSE results of the analysed case studies are validated through direct comparison against the PSCAD/EMTDC^® solution, obtaining a close agreement between the TDHSE and simulator responses.

This paper presents a systematic approach to study the dominant electromechanical oscillatory modes in a large-scale coal-fired thermal power plant. The proposed methodology first involves analysing the fault recorder data. Next a detailed linearized model is developed, and the oscillatory modes are identified via the eigenvalue analysis. Computations of the participation factors and mode shapes are useful in identifying the contributing sources on the oscillatory modes and their types. The importance of verifying the accuracy of the dynamic data used in the simulation environment for practical power systems and validation of the linear model with nonlinear simulation are highlighted in the paper. The proposed methodology is applied to a 900 MW coal-fired real power plant in Sri Lanka power system. A dominant oscillatory mode of frequency 1.2 Hz where the coal power plant oscillates against the network is identified using the proposed methodology. This systematic approach can be applied to any real power system including renewable energy sources using correct dynamic models.

Recent interest in Power Quality (PQ) enhancement and control strategies for DC/AC converters in grid-tied and islanded modes has surged. Conventional single-loop and multi-loop control topologies using PI, PR, and hysteresis controllers are widely used for system stability, current control, and PQ improvement. However, these topologies face significant challenges under non-ideal conditions, such as inverters with nonlinear or unbalanced loads. This paper addresses the challenges of simultaneous harmonic compensation and decoupled active and reactive power reference tracking by proposing a novel Hybrid SOGI-Resonant Controller (HSRC). The HSRC is designed to adapt to changing load patterns and mitigate harmonics generated by nonlinear loads. It incorporates a Second Order Generalized Integrator (SOGI) into a synchronously rotating reference frame (SRRF)-based PI-control scheme. Acting as a notch filter, the SOGI improves reference generation based on load current harmonics. The HSRC combines the benefits of Resonant controllers with cascaded PI controllers without frequent reference frame conversions. The proposed topology demonstrates significant improvement in grid-tied inverter (GTI) performance with nonlinear loads and unbalanced load combined, eliminating the need for separate controllers for positive and negative sequence signals and reducing computational burden. It provides independent control of active and reactive power alongside harmonic compensation, ensuring seamless operation under nonlinear loading conditions. The paper details the design and tuning methodology of the HSRC and verifies its efficacy through the grid-tied operation of a three-phase GTI with different loads. Results show a substantial reduction in total harmonic distortion, highlighting the HSRC's potential for enhancing power quality in practical applications.

The precision of the ultra-short-term PV power prediction is crucial for the grid to operate safely and steadily and for PV electricity to be connected on a broad scale. A combination model of ultra-short-term PV prediction based on an attention mechanism is proposed to increase the prediction accuracy of PV output power under various weather circumstances. First, using a Pearson correlation coefficient analysis, important climatic variables closely associated with PV power generation are selected and normalized monthly. The sky condition factor (SCF), a classification index, is computed using a weighted summation. This reduces the dimensionality of the input variables and eliminates seasonal influence on weather classification and the coupling interactions among various meteorological elements. Second, an unsupervised clustering of SCFs using a self-organizing map (SOM) neural network is used to classify three types of weather. After that, convolutional neural networks (CNNs) prediction models are built for each of the three types of weather. The efficient channel attention (ECA) module is then added, allowing the model to focus on key feature information and increase prediction accuracy by adaptively assigning phase weights to each of the multiple channels of feature information that the CNN has extracted. Lastly, the efficacy of the suggested prediction model is verified by simulations run on historical observed data, which demonstrate an improvement in the prediction models accuracy under various weather conditions when compared to the model without the ECA module.

Electrical load forecasting is a core element reflecting the operating conditions of the electricity system and a key tool responding to the demand of the electricity market. Achieving accurate short-term load forecasts remains a challenge due to the dynamic and non-stationary characteristics of the load data. Previous studies have mostly analyzed electrical load transformations from a single perspective. This approach often overlooks the dynamic diversity across different frequencies and the comprehensive effects of multi-time scale and granularity information. Research in electrical load forecasting has frequently failed to fully integrate multi-granularity perspectives. In this study, we introduce a novel approach, multi-granularity time-augmented learning (MTAL), to enhance the precision of short-term electrical load forecasting. Since the degree of dynamic change of different granularity information is overly influenced by time features, we design a time-augmented block to learn time representation and apply it to all granularity information to represent multi-granularity electrical load more reasonably. Furthermore, we incorporate an attention mechanism into the model, which serves to mitigate information redundancy and bolster its generalization capabilities. We evaluated our method on a univariate electrical load dataset and a multivariate electrical load dataset, respectively, and compared its performance with existing forecasting models. Experiments demonstrate that the MTAL model performs well in capturing load variation information and achieves better performance in both univariate and multivariate short-term electric load forecasting tasks. Compared to existing methods, our proposed model improves the prediction accuracy by 10(%) and reduces the computation time by 18(%).

In DC microgrids the impedance interaction takes place due to the cascaded connection of a Permanent Magnet Synchronous Generator -Voltage Source Converter and a Dual Active Bridge converter. This impedance interaction adversely degrades system stability and transient response, resulting in oscillations and voltage deviations and affecting power flow in the DC microgrid. To mitigate these challenges, a modified control strategy is proposed, that integrates an interval type-2 fuzzy logic controller (IT2FLC) with an active voltage stabilizer (AVS) and active damping (AD). The modified controller regulates voltage, current transients, and power flow more effectively than a conventional controller. The IT2FLC enhances microgrid stability by handling system uncertainties, non-linearities, and impedance interactions of cascaded systems. The AVS ensures rapid and accurate voltage regulation during transient conditions, helping to maintain a consistent voltage despite sudden changes in load. At the same time, AD suppresses oscillations, preventing resonance and ensuring smooth operation. The modified controller (IT2FLC+AVS+AD) is also compared with different controllers like PI, (PI+AD), and (PI+AVS+AD) in terms of transient parameters that reveal the modified controller is better in terms of rise time, overshoot, undershoot, and settling time.

Accurate wind power forecasting assumes an important role in power system operation and economic planning, particularly in Senegal’s flagship wind farm, the largest in West Africa. The fundamental volatility, intermittent nature, and unexpected character of wind power make it difficult to maintain power system stability. To address these challenges, an attention mechanism-based deep learning model is proposed to anticipate wind power in the short term with the goal of improving forecasting accuracy. The dynamic shifts in the wind power dataset are first processed by convolutional neural networks to extract multi-dimensional features. After being extracted, the feature vectors are placed into a long short-term memory (LSTM) network by being transformed into a series structure. Next, to optimize and improve the forecast accuracy of the model, an attention mechanism is included by assigning distinct weights to each hidden layer in the LSTM network. Real operational wind power generation data from the wind farm is utilized to verify the effectiveness of the proposed method. The results show that the proposed method can successfully boost the forecasting accuracy of wind power with better performance compared to other machine learning and deep learning models. This study not only contributes to improving wind power generation management and power system operations in Senegal but also serves as a valuable reference for promoting renewable energy transitions across sub-Saharan Africa.

This research presents a hybrid technique named EOO–RERNN, integrating the Eurasian oystercatcher optimizer (EOO) and Recalling enhanced recurrent neural network (RERNN), to enhance fault tolerance in Matrix converters (MCs) for Induction Motors (IMs). The proposed method assesses fault impacts, reconstructs healthy phases, manages switching frequency with Space vector modulation (SVM), and diagnoses faults to optimize switching states. Comparative analysis using MATLAB/Simulink shows a 1.1% reduction in torque ripple compared to existing methods like the Cuckoo Search Algorithm and Particle Swarm Optimization, demonstrating superior performance and improved motor reliability.

This manuscript investigates the optimal placement and sizing of Photovoltaic (PV) systems within electrical distribution networks. The problem is formulated as a multiobjective optimization, seeking to simultaneously minimize power losses and enhance voltage profiles while accounting for uncertainties in PV power output, variations in consumer load demand, and the impact of PV inverter-induced harmonic current injection on power quality. The optimal solution is obtained via a Mixed-Integer NonLinear Programming (MINLP) approach, leveraging the Basic Open-source Nonlinear Mixed-Integer programming (BONMIN) solver embedded within the General Algebraic Modeling Systems (GAMS) platform. The performance of the proposed BONMIN-based methodology is evaluated through two case studies. In the first case, the BONMIN solver is employed for the optimal allocation and sizing of 1, 2, and 3 PVs in the IEEE 33-bus test system. The obtained optimal solutions are compared with those from popular metaheuristic algorithms—Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), and Bat Algorithm (BAT), in terms of both objective function minimization and numerical efficiency. The results in the first case showed that 3 optimally placed PVs contributed to a 26.46% loss reduction and 38.18% voltage deviation reduction. The results demonstrate the superiority of the proposed approach, which achieves better optimal solutions with enhanced computational performance relative to metaheuristic alternatives. In the second case, the BONMIN solver is applied to the optimal PV integration problem in the real-world “Bijela” distribution network in Montenegro, where the results show that the optimal placement of 3 PVs contributes to a 22.49% loss reduction and a 28.14% voltage deviation reduction. Furthermore, the findings in the second case confirm the applicability of the BONMIN solver for optimal PV integration in realistic distribution network environments. Additionally, the simulation results indicated minimal negative impacts of optimally allocated and sized PVs on the power quality of the distribution network for both test systems.

Photovoltaic (PV) panels are prospective for sunlight to direct electrical energy using the photovoltaic effect. Overheating of PV panels is influenced to limiting the solar performance, and innovative bifacial panel technique found better heat build-up leads to reduced lifespan and costlier reasons. The present research focuses on limiting the PV panel temperature by the implementation of the porous medium and nanofluid, which also assists in enhancing the thermal efficiency of the solar collector system. The alumina (Al₂O₃) and silicon dioxide (SiO₂) nanoparticles were dispersed within the water with a volume fraction of 0.5% through the ultrasonication method, and the porous medium was made of silicon carbide material. Furthermore, the hybrid of both Al₂O₃ and SiO₂ was introduced to study the effect of combined nanofluids and porous medium in PV panel cooling. Based on experimentation, heat gain, electrical power, total power by PV, thermal efficiency, electrical efficiency, and exergy efficiency were calculated. The peak fluid temperature, heat gain, electrical power, and total power by hybrid nanofluid are about 72.4 °C, 534.2 W, 221.4 W, and 258.6 W, respectively. Furthermore, the average thermal, electrical, and exergy efficiency is about 59.8%, 8.8%, and 7.1% by hybrid nanofluid with porous medium. Hence, the hybrid nanofluid and porous medium integration shows peak PV performance and higher thermal and electrical efficiency than other fluid conditions.