Stability Performance Of System Research Articles

Review of photovoltaic power prediction based on artificial intelligence methods

With the continuous growth of global demand for renewable energy, photovoltaic power generation, as a clean and efficient form of energy, has received widespread attention. However, photovoltaic power is affected by various factors such as irradiance, temperature, resulting in significant fluctuations in its output power. Therefore, accurate prediction of photovoltaic power is of great significance for the stable operation of the power system. In recent years, the rapid development of artificial intelligence technology has provided new solutions for photovoltaic power prediction. This paper will review the photovoltaic power prediction based on artificial intelligence methods, introduce its basic principles, application status, and challenges, in order to provide useful insights in related fields.

A Fusion Tracking Algorithm for Electro-Optical Theodolite Based on the Three-State Transition Model.

This study presents a novel approach to address the autonomous stable tracking issue in electro-optical theodolite operating in closed-loop mode. The proposed methodology includes a multi-sensor adaptive weighted fusion algorithm and a fusion tracking algorithm based on a three-state transition model. A refined recursive formula for error covariance estimation is developed by integrating attenuation factors and least squares extrapolation. This formula is employed to formulate a multi-sensor weighted fusion algorithm that utilizes error covariance estimation. By assigning weighted coefficients to calculate the residual of the newly introduced error term and defining the sensor's unique states based on these coefficients, a fusion tracking algorithm grounded on the three-state transition model is introduced. In cases of interference or sensor failure, the algorithm either computes the weighted fusion value of the multi-sensor measurement or triggers autonomous sensor switching to ensure the autonomous and stable measurement of the theodolite. Experimental results indicate that when a specific sensor is affected by interference or the off-target amount cannot be extracted, the algorithm can swiftly switch to an alternative sensor. This capability facilitates the precise and consistent generation of data, thereby ensuring the stable operation of the tracking system. Furthermore, the algorithm demonstrates robustness across various measurement scenarios.

Evaluation Method for Voltage Regulation Range of Medium-Voltage Substations Based on OLTC Pre-Dispatch

A new energy industry represented by photovoltaic and wind power has been developing rapidly in recent years, and its randomness and volatility will impact the stable operation of the power system. At present, it is proposed to enrich the regulation of the power grid by tapping the regulation potential of load-side resources. This paper evaluates the overall voltage regulation capability of substations under the premise of considering the impact on network voltage security and providing a theoretical basis for the participation of load-side resources of distribution networks in the regulation of the power grid. This paper proposes a Zbus linear power flow model based on Fixed-Point Power Iteration (FFPI) to enhance power flow analysis efficiency and resolve voltage sensitivity expression. Establishing the linear relationship between the voltages of PQ nodes, the voltage of the reference node, and the load power, this paper clarifies the impact of reactive power compensation devices and OLTC (on-load tap changer) tap changes on the voltages of various nodes along the feeder. It provides theoretical support for evaluating the voltage regulation range for substations. The day-ahead focus is on minimizing network losses by pre-optimizing OLTC tap positions, calculating the substation voltage regulation boundaries within the day, and simultaneously optimizing the total reactive power compensation across the entire network. By analyzing the calculated examples, it was found that a pre-scheduled OLTC (on-load tap changer) can effectively reduce network losses in the distribution grid. Compared with traditional methods, the voltage regulation range assessment method proposed in this paper can optimize the adjustment of reactive power compensation devices while ensuring the voltage safety of all nodes in the network.

Optimization of Ground Source Heat Pump System Based on TRNSYS in Hot Summer and Cold Winter Region

A cooling tower-assisted ground source heat pump system is proposed to solve the problem of soil heat accumulation in areas with hot summers and mild winters. TRNSYS is used to establish the simulation model of the ground source heat pump system. Taking the ground source heat pump demonstration project in Nanchang City as the research object, the operation effect of the ground source heat pump is simulated and analyzed. The key parameters of the optimization system are proposed, and the operation time of the cooling tower is controlled. The optimization results show that the cooling tower can not only improve the system performance in a short time but also enable long-term stable and efficient operation of the refrigeration system, the compound ground source heat pump system optimized by the cooling tower has almost no soil heat accumulation, and the system performance has been improved. The optimal running time of the cooling tower is 3216~4344 h and 5424~6696 h, and the system running effect is the best at this time. The research results can provide a theoretical basis for the optimal design of the cooling tower auxiliary ground source heat pump system.

Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer

Accurate short-term load forecasting is critical for enhancing the reliability and stability of regional smart energy systems. However, the inherent challenges posed by the substantial fluctuations and volatility in electricity load patterns necessitate the development of advanced forecasting techniques. In this study, a novel short-term load forecasting approach based on a two-stage feature extraction process and a hybrid inverted Transformer model is proposed. Initially, the Prophet method is employed to extract essential features such as trends, seasonality and holiday patterns from the original load dataset. Subsequently, variational mode decomposition (VMD) optimized by the IVY algorithm is utilized to extract significant periodic features from the residual component obtained by Prophet. The extracted features from both stages are then integrated to construct a comprehensive data matrix. This matrix is then inputted into a hybrid deep learning model that combines an inverted Transformer (iTransformer), temporal convolutional networks (TCNs) and a multilayer perceptron (MLP) for accurate short-term load forecasting. A thorough evaluation of the proposed method is conducted through four sets of comparative experiments using data collected from the Elia grid in Belgium. Experimental results illustrate the superior performance of the proposed approach, demonstrating high forecasting accuracy and robustness, highlighting its potential in ensuring the stable operation of regional smart energy systems.

Economic Load Forecasting based on IPSO-VMD-DBiLSTM Network

Accurate short-term load forecasting is vital for the stable operation of power systems. Given the highly volatile and nonlinear nature of load sequences, we propose a novel Deep Bidirectional Long Short-Term Memory (DBiLSTM) network incorporating Variational Mode Decomposition (VMD) and an attention mechanism to address the aforementioned challenges. In this study, the model's hyperparameters are optimized using an Improved Particle Swarm Optimization (IPSO) technique. Notably, IPSO employs a nonlinearly decreasing inertia weight to overcome the drawbacks of premature convergence and local optima.

Optimize the ratio of immobilized gel materials to improve gel performance and oxygen mass transfer rate: achieve rapid start-up and efficient and stable operation of partial nitration

A gel filler with diatomaceous earth, activated carbon, and CaCO3 compounded with polyvinyl alcohol was prepared and optimized. Rapid start-up and efficient and stable operation of partial nitrification (PN) was achieved by immobilization of conventional activated sludge. The results showed that the proper material proportion synergistically improved the performance and oxygen mass transfer rate of the gel. The continuous flow reactor was operated for 10d to successfully start PN, and the maximum ammonia oxidation rate of this filler was 58.30mg·(L·h)-1 during the stable operation, and the nitrite accumulation rate was maintained above 90%. However, due to the greater mass transfer resistance of unoptimized fillers, more dissolved oxygen waste and nitrogen loss were caused, resulting in delayed expression of biological activity. Based on the kinetic characterization of ammonia-oxidizing bacteria (AOB) within the filler and high-throughput sequencing, it was demonstrated that the rapid start-up and efficient and stable operation of the PN system was due to the highly oxygenophilic and community-dominant position of AOB within this filler. This study provides new options and insights at the level of immobilization technology for the realization and efficient and stable operation of PN.

Sensitivity Analysis and Distribution Factor Calculation under Power Network Branch Power Flow Exceedance

As the scale of power systems continue to expand and their structure becomes increasingly complex, it is likely that branch power flow exceedance may occur during the operation of power systems, posing threats to the safe and stable operation of entire systems. This paper addresses the issue of branch flow exceedance in power networks. To enhance the operational efficiency and optimize the adjustment effects, this paper proposes a method for eliminating branch power flow exceedance by improving the particle swarm optimization (PSO) algorithm through the introduction of sensitivity and distribution factors. Firstly, it introduces the basic theory and calculation methods of sensitivity analysis, focusing on deriving the calculation principles of power flow sensitivity and voltage sensitivity, used to predict the responses of power flow at each branch in the power network to power or voltage changes. Subsequently, the paper provides a detailed derivation of the calculation principles for the line outage distribution factor (LODF), which effectively assesses the changes in branch power flow in the power network under specific conditions. Finally, a method for eliminating branch power flow exceedance based on a combination of sensitivity analysis and PSO algorithm is proposed. Through case analysis, it is demonstrated how to use the sensitivity and distribution factor to predict and control the power flow exceedance issues in power systems, verifying the efficiency and practicality of the proposed method for eliminating branch power flow exceedance. The study shows that this method can rapidly and accurately predict and address branch power flow exceedance in power system, thereby enhancing the operational safety of the power system.

An efficient 3D tube-level flux-thermal model of external tubular receivers in solar power tower systems

Accurate and efficient radiative flux simulation and thermal analysis of liquid tubular receivers are essential for safe and stable operations of solar power tower systems. However, the complicated structure and photothermal processes on these receivers pose significant challenges for simulation. Although existing models have made great simplifications, the simulation results are still inefficient and inaccurate. To address these issues, a novel and universal 3D flux-thermal model is proposed. Firstly, a flux model is proposed via a tube-level Monte Carlo ray tracing method based on GPU parallel computing, enabling real-time and accurate radiative flux density distribution simulation for each absorber tube. Secondly, a high-resolution multi-tube thermal model is proposed, providing accurate 3D thermal analysis for each absorber tube. Through pre-factorization and highly parallelized designs, the proposed thermal model achieves a 600 times acceleration in efficiency, even with tens of millions of discrete elements. Finally, the novel flux-thermal model is validated against publicly published measured data from Solar Two, indicating a mean deviation of only 0.1% and a root mean square error of 0.006, demonstrating its high accuracy. Furthermore, the proposed flux-thermal model reveals complex and asymmetrical flux and temperature distributions on the absorber tube, in contrast to the symmetrical distributions obtained by the simplified models. Such inaccuracies in the simplified simulations may cause operational misjudgments and affect system safety potentially. Therefore, the proposed 3D tube-level flux-thermal model provides an accurate and efficient solution to radiative flux simulation and thermal analysis, enhancing the safe and stable operation of solar power tower systems.

Fault analysis of oil-immersed transformer based on digital twin technology

Oil-immersed transformer is one of the important equipment to support the safe and stable operation of power system. At the same time, in the process of transformer operation, transformer failure is inevitable, this kind of failure belongs to multiple latent faults, which seriously endangers the safe operation of the transformer. Therefore, the maintenance of the transformer has played a crucial role. A fault detection method based on digital twin model is proposed to solve the problem that the internal dynamic characteristics of oil-immersed transformer are difficult to extract, and the fault detection error is large due to noise interference. The dynamic model of oil-immersed transformer is constructed, the main cause of failure is analysed, the failure time of internal insulator is taken as a linear index, and the linear correlation between failure probability and failure time is solved by Weibull distribution function. The fitting function between transformer fault residence time, operating days and fault probability variance is established, and the fitting value obtained is used as the initial data reference of the fault detection model. The digital twin model is used to establish the dynamic fault probability calculation function, and the on-site data is substituted to calculate the fault detection threshold, and then the data is compared to complete the detection.

Performance Test of Artificial Defoliating Broccoli Conveyor Line and Analysis of Defoliating Broccoli Inflorescences

There is a close relationship between stem and leaf biological characteristics of mature broccoli plants and defoliation technology. Morphological parameters such as the spread degree and diameter of cauliflower stem and leaf and the connection performance of cauliflower stem and leaf were studied. These experiments are helpful to the research of defoliation devices and methods for large-scale manual transportation lines. In this paper, according to the damage forms that may be suffered in the separation process of mature broccoli stems and leaves, “sunny” broccoli varieties were selected. Firstly, the mechanical properties of mature broccoli stems were obtained through different loading methods such as stretching, compression, shearing and bending. Secondly, the stress relaxation characteristics of broccoli were analyzed by static compression of broccoli flower balls. Finally, the separation method of broccoli stems and leaves was simulated by ANSYS. The simulation results showed that shear fracture was more suitable for stem and leaf separation of broccoli than tensile fracture. Then, aiming at the separation of stems and leaves of broccoli, an artificial broccoli assembly line was proposed to ensure the efficiency of leaf removal through man–machine cooperation. The dynamic characteristics of the belt of broccoli leaf removal line were studied to ensure the efficient and stable operation of the conveyor system of broccoli artificial leaf removal line.

Study on photovoltaic power prediction with TSO-LSTM-XGBoost coupled model accounting for weather factors

ABSTRACT The explicit prediction of PV power itself is of great significance to the scheduling and operation of the power grid. To ensure the stable operation of the power system, this paper proposes a coupled model for PV power prediction based on TSO-LSTM-XGBoost. This model considers the weather factor using the tuna swarm algorithm(TSO) to optimize the long and short-term memory network model(LSTM) to overcome the shortcomings of blindness and time-consuming in the process of randomly selecting the parameters of the LSTM model. At the same time, using the extreme gradient boosting model (XGBoost), the algorithm is improved and corrected for the large prediction error in cloudy and rainy weather, and the weighted error method is used to couple the model to obtain the final prediction results. Finally, the accuracy of the proposed model is verified by comparing the PV system and meteorological data of a certain region in Shenzhen, China. The results show that the proposed TSO-LSTM-XGBoost coupled model has a value of 71.98 for MAE in cloudy days and 82.09 for MAE in rainy days, and the prediction accuracy is better than PSO-LSTM and LSTM.

Adsorption and sensing properties of TiO2-modified MoSe2 monolayer for SF6 decomposition components based on the first-principles

As crucial components in power systems, SF6 gas-insulated equipment inevitably develops insulation defects and faults over long-term operation, leading to the decomposition of SF6 gas. Therefore, detecting SF6 decomposition components is essential for assessing the operational status of SF6 gas-insulated equipment and ensuring the safe and stable operation of power systems. Based on first principles, the modification mechanism of TiO2 on MoSe2 was explored, and optimal adsorption models for gases SO2, H2S, SOF2, and SO2F2 on this composite material were established. The adsorption characteristics of each system were analyzed through parameters such as adsorption energy, adsorption distance, charge transfer, electron density difference, and density of states, and the sensing properties of the systems were discussed using parameters including changes in frontier molecular orbitals, desorption time, and sensitivity. The results indicate that TiO2–MoSe2 has difficulty capturing SO2F2, but exhibits chemisorption for SO2, H2S, and SOF2 with adsorption energies of −1.25 eV, −0.99 eV, and −3.40 eV, respectively. Upon adsorption of SO2, TiO2–MoSe2 shows a significant change in electrical conductivity, a desorption time of 4.51 s (at 498 K), and a strong sensitivity response. However, the response to H2S is relatively weak, with lower sensitivity. Therefore, this material is suitable for detecting SO2 but not H2S. TiO2–MoSe2 demonstrates a significant response and excellent sensitivity to SOF2; however, the adsorption process lacks repeatability. Therefore, it can be used as a disposable gas sensor or solid adsorbent for SOF2 detection. The results of this study provide theoretical support for the application of MoSe2-based sensors in the monitoring and fault diagnosis of SF6 gas-insulated equipment.

MGMI: A novel deep learning model based on short-term thermal load prediction

Accurate heat load prediction is the key to stable operation and control of district heating system. However, current heat load forecasting methods tend to treat individual buildings as isolated entities, ignoring the temporal and spatial correlation between buildings. In this paper, a heat load forecasting model is proposed which fully considers spatiotemporal information. Firstly, the spatial and temporal relationship between buildings is analyzed by statistical method. Secondly, synchronous wavelet transform (SWT) is used to denoise the heat load data to reduce the difficulty of prediction. Then, a hybrid model of multi-modal graph attention network (MG) and multi-scale Informer (MI) is constructed to capture spatiotemporal information in the data. Finally, an example of DHS in a campus in Liaoyang is analyzed. The results showed that compared with LSTM, CNN-Informer, GCN-LSTM and GAT-Informer, the MAE of MGMI on 168 h test set was reduced by 71.4%, 61.5%, 58% and 41.6%, respectively. And has the highest computational efficiency. The validity of the model is verified, which provides an important basis for the operation control of the thermal system.

Refractory wastewater shapes bacterial assembly and key taxa during long-term acclimatization

Bacterial assembly and key taxa during long-term acclimatization in refractory wastewater treatment systems is of paramount importance for optimizing system performance and improving management strategies. Therefore, this study employed high-throughput sequencing, coupled with machine learning models and statistical analysis approaches, to comprehensively elucidate key features of bacterial communities and assembly processes in pesticide wastewater treatment systems. A nine-month monitoring showed substantial variation in diversity and composition of bacterial community between two interconnected biological treatment units (designated as BA and PA). Dynamics of bacterial communities in both units were similar. Moreover, water quality played crucial roles in regulating the bacterial community structure of activated sludge, which were primarily driven by deterministic patterns. Homogeneous selection contributed to 62.85 % and 64.43 % of the variations in BA and PA samples, respectively. Additionally, network analysis revealed significant modularity in bacterial compositions in both groups. Linear regression analysis identified major bacterial modules associated with metabolism and degradation functions. Notably, Module 2 in PA samples has significant positive correlations with functions related to metabolism of nucleotide, amino acid, and xenobiotics, as well as benzoate degradation. Furthermore, key taxa in ecological modules identified by Random Forest model, such as Pseudomonas, Sphingobium, and PHOS-HE28, were dominant populations with metabolism and degradation functions. Particularly, Sphingobium, appeared to be a potential multifunctional degrading bacterium, related to amino acid and xenobiotics metabolism, as well as fatty acid, valine, leucine, isoleucine, fluorobenzoate, and aminobenzoate degradation. These findings are important for developing operating strategies to maintain stable system performance during refractory wastewater treatment.

Optimizing micro power generation with blended fuels and porous media for H2-fueled combustion

To enhance the combustion stability and thermal performance of micro-thermophotovoltaic system, a combustor inserted porous media and varying channel heights for premixed H2/CH4/Air burning is proposed. The experimental and numerical methods are conducted to investigate the effects of CH4 addition, equivalence ratio, porous media, and chamber setting on combustion characteristics, heat transfer, and flame stability. The results indicate that a small fraction of CH4 addition and porous media insertion both enhance heat release and transfer of H2-fueled combustion, and the optimal CH4 blending ratio is influenced by chemical energy input. Appropriately increasing the equivalence ratio under high flow rates contributes to stabilize the flame and improve the combustor thermal performance. Furthermore, porous media effectively promotes flame stability and heat transfer efficiency, with longer porous media enhancing heat conduction and convection processes, thereby increasing the radiation temperature. Besides, increasing the channel height of the porous media combustor appropriately enhances the electrical power output of the power system. Besides, the maximum electrical output of 9.7 W is obtained in the porous media combustor with a channel height of 11 mm at mc = 20 %, Ф = 1.0 and mf = 9.448 × 10−5 kg/s, which is 7.2 W higher than the free flame combustor with h = 7 mm.

A numerical analysis of wave type and steepness effects on cylindrical buoy dynamic characteristics

In the intricate realm of ocean-atmosphere dynamics, the sustained and stable operation of buoy observation systems under rough seas is crucial for ensuring marine resource security. This study employs a coupled Level-Set and Volume of Fluid (CLSVOF) method alongside the immersed boundary method. It investigates the physical processes of wind-wave interaction, the dynamic characteristics of cylindrical buoys, as well as the underlying mechanisms. The research highlights the significant impact of wave types (wind waves, swells) and wave steepness on the buoy's drift and oscillatory behavior. An increase in wave steepness and a shift from wind waves to swell markedly intensify both the buoy's drift and its oscillatory motions. Additionally, a thorough analysis of the loads on the buoy under varying maritime conditions is performed. By examining the vertical momentum equation on the windward and leeward sides, the secondary load cycle phenomenon is found to originate from collapse of the water column formed by the convergence of water flow behind the buoy. Wind waves are found to be less conducive to secondary load cycles compared to swell waves. Furthermore, the study investigates the relationship between the drag coefficient and the wave crest height on the windward side of the buoy, revealing a robust negative correlation between the two. This research not only illuminates the complex dynamics of wind-wave couplings but also offers theoretical insights for the optimization of buoy design.

Efficient alleviation granular sludge floatation in a high-rate anammox reactor by dosing folate

Although granular floatation has been recognized as a significant issue hindering the application of high-rate anammox biotechnology, limited knowledge is available about its causes and control strategies. This study proposed a novel control strategy by adding folate, and demonstrated its role in the granular floatation alleviation through long-term operation and granular characterizations. It was found that the floatation of anammox granular sludge was obviously relieved with the decreased sludge floatation potential by 67.1% after dosing with folate (8 mg/L) at a high nitrogen loading rate of 12.3 kg-N/(m3·d). Physiochemical analyses showed that the decrease of extracellular polymeric substances (EPS) content (mainly protein), the alleviation of granular surface pore plugging in conjunction with the smooth discharge of generated nitrogen gas were collectively responsible for efficient floatation control. Moreover, metagenomic analysis suggested that the synergistic interactions between anammox bacteria and their symbionts were attenuated after dosing exogenous folate. Anammox bacteria would reduce their synergistic dependence on the symbionts, and decline the supply of metabolites (e.g., amino acids and carbohydrates in EPS) to symbiotic bacteria. The declined EPS excretion contributed to the alleviation of granular floatation by dredging pores blockage, thus leading to a stable system performance. The findings not only offer insights into the role of microbial interaction in granular sludge floatation, but also provide a feasible approach for controlling the floatation issue in anammox granular-based processes.

Prediction of rainy-day photovoltaic power generation based on Generative Adversarial Networks and enhanced Sparrow Search Algorithm

Accurate and timely photovoltaic (PV) power forecasting is crucial for the stable operation of power systems. To address the issue of sparse PV power data on rainy days, this paper proposes the use of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to augment rainy-day data. A progressive gradient penalty strategy is introduced to avoid gradient vanishing. A Bidirectional Long Short-Term Memory network is employed for power forecasting, with adaptive hyperparameter optimization achieved through an improved Sparrow Search Algorithm (LSSA). The optimization capability is enhanced using a chaotic mapping strategy. Validation on data from a 1 MW PV plant demonstrates that the LSSA-optimized model achieves the best forecasting results. After augmenting the rainy-day dataset, the Mean Absolute Error and Root Mean Square Error decreased by 15.3 % and 6.15 %, respectively, indicating that WGAN-GP significantly improves forecasting accuracy on rainy days.

Research on prediction method of photovoltaic power generation based on transformer model

Accurate prediction of photovoltaic power generation is of great significance to stable operation of power system. To improve the prediction accuracy of photovoltaic power, a photovoltaic power generation prediction machine learning model based on Transformer model is proposed in this paper. In this paper, the basic principle of Transformer model is introduced. Correlation analysis tools such as Pearson correlation coefficient and Spearman correlation coefficient are introduced to analyze the correlation between various factors and power generation in the photovoltaic power generation process. Then, the prediction results of traditional machine learning models and the Transformer model proposed in this paper were compared and analyzed for errors. The results show that: for long-term prediction tasks such as photovoltaic power generation prediction, Transformer model has higher prediction accuracy than traditional machine learning models. Moreover, compared with BP, LSTM and Bi-LSTM models, the Mean Square Error (MSE) of Transformer model decreases by 70.16%, 69.32% and 62.88% respectively in short-term prediction, and the Mean Square Error (MSE) of Transformer model decreases by 63.58%, 51.02% and 38.3% respectively in long-term prediction, which has good prediction effect. In addition, compared with the long-term prediction effect of Informer model, Transformer model has higher prediction accuracy.