Power Supply Department Research Articles

Impact of supply–demand matching on citizen satisfaction: analysis based on expectancy disconfirmation model

In the context of ensuring the well-being of citizens, the energy industry should predominantly adhere to the people-oriented approach. Investigation of citizen satisfaction through the lenses of power supply and societal demand is of utmost importance. This study constructs a satisfaction model based on expectancy disconfirmation theory, employs response surface analysis to explore the impact of matching degree between demand and supply on citizen satisfaction. The results show that citizen satisfaction is notably higher in case of a match between public expectations and supply quality compared to mismatch scenarios. Furthermore, instances involving both high public expectations and high supply quality, yield greater satisfaction compared to scenarios involving low public expectations and low supply quality. Importantly, citizen satisfaction experiences a dip when public expectations surpass supply quality, unlike situations where public expectations fall short of supply quality. Policy awareness does not moderate the effect of matching analysis on citizen satisfaction. This research enriches the scope of understanding the complex nature of public satisfaction, offering theoretical support for the high-quality development of power supply departments, catering to both energy needs and well-being of citizens. Thus, the current study significantly contributes to promoting the sustainable and high-quality development of the local social economy.

Electric Load Forecasting Based on Deep Ensemble Learning

Short-to-medium-term electric load forecasting is crucial for grid planning, transformation, and load scheduling for power supply departments. Various complex and ever-changing factors such as weather, seasons, regional economic structures, and enterprise production cycles exert uncontrollable effects on the electric grid load. While the causal convolutional neural network can significantly enhance long-term sequence prediction, it may suffer from problems such as vanishing gradients and overfitting due to extended time series. To address this issue, this paper introduces a new power load data anomaly detection method, which leverages a convolutional neural network (CNN) to extract temporal and spatial information from the load data. The features extracted are then processed using a bidirectional long short-term memory network (BiLSTM) to capture the temporal dependencies in the data more adeptly. An enhanced random forest (RF) classifier is employed for anomaly detection in electric load data. Furthermore, the paper proposes a new model framework for electricity load forecasting that combines a dilated causal convolutional neural network with ensemble learning. This combination addresses issues such as vanishing gradients encountered in causal convolutional neural networks with long time series. Extreme gradient boosting (XGBoost), category boosting (CATBoost), and light gradient boosting machine (LightGBM) models act as the base learners for ensemble modeling to comprehend deep cross-features, and the prediction results generated by ensemble learning serve as a new feature set for secondary ensemble modeling. The dilated convolutional neural network broadens the receptive field of the convolutional kernel. All acquired feature values are concatenated and input into the dilated causal convolutional neural network for training, achieving short-to-medium-term electric load forecasting. Experimental results indicate that compared to existing models, its root mean squared error (RMSE) and mean squared error (MSE) in short-term and mid-term electricity load forecasting are reduced by 4.96% and 12.31%, respectively, underscoring the efficacy of the proposed framework.

Research on the simulation of SVG for online de-icing on distribution lines

The distribution network connects with families, more branch circuit breakers, more and more users rely on power in winter, which requires higher and higher reliability requirements for power supply. The power supply department is facing greater pressure to stop distribution line for de-icing. In view of the above reasons, this paper first briefly introduces several measures of de-icing without power failure, and selects the technical route of injecting reactive power at the end of the line for online de-icing; secondly, it introduces the principle of SVG device generating reactive power, which uses reactive power to generate heat loss in the distribution network line for on-line de-icing, and then proposes an improved reactive power outer loop control strategy to make it equivalent to current source De-icing device; Finally, a simulation model is built in PSCAD. Taking a 5km ACSR (LGJ-120) line with 5.4Mvar reactive power injected at the end of the line as an example, it can be seen that the line current reaches more than 500A due to the injected reactive power. The simulation results prove the effectiveness of the method. Considering its economy, it provides a feasible way for the distribution network with important loads Electric deicing scheme.

Experimental and analysis on household electronic power consumption

Household power consumption helps the power supply department understand the power consumption of residents and whether there will be some abnormal power consumption phenomena. Taking the individual household electric power consumption dataset as an example, this paper establishes an extensible experimental analysis framework and analyzes the data in a visual way.In the experiment, the effects of linear regression model and neural network model on different characteristics are compared. The experiment shows that the effect of neural network model is better than linear regression model in the experimental dataset.

Research on Flashover Voltage Prediction of Catenary Insulator Based on CaSO4 Pollution with Different Mass Fraction

Pollution flashover accidents occur frequently in railway OCS in saline-alkali areas. To accurately predict the pollution flashover voltage of insulators, a pollution flashover warning should be made in advance. According to the operating environment of insulators along the Qinghai-Tibet railway, the pollution flashover experiments were designed for the cantilever composite insulator FQBG-25/12. Through the experiments, the flashover voltage under the influence of soluble contaminant density (SCD) of different pollution components, non-soluble deposit density (NSDD), temperature (T), and atmospheric pressure (P) was obtained. On this basis, the GA-BP neural network prediction model was established. P, SCD, NSDD, CaSO4 mass fraction (w(CaSO4)), and T were taken as input parameters, 50% flashover voltage (U50%) of the insulator was taken as output parameters. The results showed that the prediction deviation was less than 10%, which meets the basic engineering requirements. The results could not only provide early warning for the anti-pollution flashover work of the railway power supply department, but also be used as an auxiliary contrast to verify the accuracy of the results of the experiments, and provide a theoretical basis for the classification of pollution levels in different regions.

Classification and Identification of Power Quality Disturbances Based on Stochastic Forest Model

With the emergence and use of a large number of new power electronic equipment, the power supply department has begun to pay extensive attention to the problem of power quality, and the majority of users have put forward higher requirements for the quality of power supply. This paper studies the classification and identification method of power quality disturbances based on random forest model. In this paper, according to IEEE power quality standard, the normal waveform and 16 common power quality disturbance waveforms are mathematically modeled, and the power quality disturbance signal is analyzed by S-transform. In this paper, the power quality disturbance identification algorithm based on random forest is optimized. Experimental data show that the optimized method has higher disturbance recognition accuracy and better anti noise ability. Therefore, using the power quality disturbance identification method proposed in this paper to monitor the power quality of power grid is of great significance to ensure the safe and stable operation of power grid and improve economic benefits.

A novel grey seasonal model based on cycle accumulation generation for forecasting energy consumption in China

For time series forecasting, the mobile holiday effect typically brings certain difficulty in obtaining accurate forecasts for monthly and quarterly series since it can result in enormous disturbances for modeling, especially for the sequences with limited information and much uncertainty. This study proposes a discrete grey seasonal model by cycle accumulation generation as an alternative approach to seasonal time series forecasting to effectively address such issues. Moreover, the logarithmic transformation technique is introduced to enhance the forecasting performance of this proposed approach, which can significantly reduce the errors resulted from the cycle accumulation generation restoration. To validate the effectiveness and practicality of the proposed model, a range of competing models, including the DGSM(1,1), SFGM(1,1), SGM(1,1), SARIMA, BP neural network, LSSVM, RBF, and LSTM models, are employed to conduct experimental comparisons. Empirical evidence from the monthly industrial electricity and quarterly natural gas consumptions show that the newly designed approach is superior to other competitors in terms of level accuracy and performance stability with the support of the logarithmic transformation technique. Further, the robustness test over the performance of the logarithmic transformation and cycle accumulation generation techniques has been implemented. Results demonstrate that the proposed approach with the logarithmic transformation technique's support can be considered a promising tool to weaken the mobile holiday’ disturbance and improve the forecasting accuracy. Therefore, based on the accurate predictions, the power supply department is able to prepare the power supply plans in advance, reducing the negative effects of the holiday effect on energy management.

Application of Time-Frequency Domain Combined Medium Response Technology in the Insulation Moisture Evaluation of Transformer Oil-Paper

In the current period, many transformers in domestic power supply departments are in the state of overdue service, there are many problems of insulation aging and increasing water in the body, and the rapid and accurate insulation moisture assessment is a technical problem that needs to be solved. The commonly used methods of medium response test for damp evaluation of transformer oil-paper insulation system include time-domain PDC and frequency-domain FDS. These two methods have their own advantages, but also have their own disadvantages. The purpose of this paper is to transform these two test methods into time-frequency combined medium response technology combining time-domain PDC method and frequency-domain FDS method, which can be used in transformer oil paper insulation moisture evaluation to improve the efficiency of transformer operation detection. In this paper, PDC method, FDS method and time-frequency combination method were used to obtain the response curves of insulating oils with different water contents, and the curves were analyzed. By comparing the results of measuring time and water content, the effectiveness of time-frequency combination method was verified. The experimental results of this paper further show that the time-frequency domain combined media response technology has a good application value in the evaluation of transformer oil paper insulation damp.

Research on Development Ideas of Customer-side Energy Internet Business of Power Grid Enterprises Based on SWOT Analysis Method

In recent years, the concepts and practices of smart cities and “Internet plus” smart energy have mushroomed, greatly promoting the development and application of customer-side energy Internet business and stimulating users’ diversified and personalized demand for interactive services of intelligent power utilization. Intelligent power utilization, as the core of the interactive service system, serves as a bridge between power supply departments and customers. Its construction has a direct impact on the efficiency, economic operation and orderly electricity consumption of power grids. In the context of the increasingly intelligent power grid and complex load structure, this paper analyzed the development trend of energy Internet and adopted SWOT to reveal the strengths, weaknesses, opportunities and challenges of power grid enterprises in developing energy Internet, explore their development ideas on energy Internet businesses, and facilitate their development of energy Internet.

Research on the charging rules of shore power service charge in China

Firstly, this paper defines the definition and attribute of shore power service charge, and combs the current situation of shore power service charge and electricity charge in China. At the same time, the paper analyses the cost of shore power service, electricity charge of the power supply department and the cost of ship's using fuel oil for self-generation, and makes a comprehensive comparison between the cost of ship's using shore power and the cost of ship's self-generation. Finally, according to the principle of ensuring that the cost of using shore power (shore power service charge plus electricity charge) is lower than the cost of self-generation for ships, the paper puts forward suggestions that the shore power service charge should be collected according to the power usage and the government should issue guidance prices.

Survey and Analysis of the Impact of Voltage Sag and Short-term Interruption on Power Users in a Region of Southern China

In order to gain a deeper understanding of the impact of voltage sag in major power users in a region of southern China, the local power supply department led the survey of the impact of voltage sag on many major power users in the region. In order to ensure that the surveyed users have a good representativeness, the selection of surveyed users takes into account factors such as geographical, industry and historical complaints. Through the analysis of the survey information, two sensitive users who were seriously affected by the voltage sag were obtained. As a whole, the present situation that power users are affected by voltage sag has been mastered.

A Comprehensive Dispatch Management System for Distributed Generation Based on Cloud-Computing Technology

At present, China has built a number of DG (distributed power generation) demonstration projects as part of intelligent cities, intelligent business parks, university campuses and residential areas. The interaction mechanism, among DGs-especially those with energy storage, the users and the distribution grid need to be clear; i.e. how to unify the coordination among solar power systems, small wind power generation systems, the battery energy reserve systems, and electrical vehicles, so as to optimize resources allocation, cut peak and compensate trough loads; how to enable future homes and business users has more flexibility, controllability on electricity usage thus to advance the technology development in the field of electricity usage. The above mentioned problems can be solved smoothly by researching on the comprehensive use of modern power electronic technologies, communication technologies, computer and network technologies, advanced sensor and measurement & control technology, to combine them together for monitoring, protection control, cloud computing , and management of power supply department for active distribution network after DG access, to constitute the comprehensive dispatching system based on the DG of cloud computing. Dispatching of DG for distance dispersion location and a huge number of terms, to based on the cloud computing is very cost-effective, and it is worthy of further study and application.

Research of Single Phase Short Fault in One-Generator Infinite Bus Power Systems

Electric power industry is related to people's livelihood, economic development, an important foundation for national construction industry. With the deepening of our country comprehensive construction, the demand has increased rapidly, at the same time also have higher requirements for quality of power supply. From the previous electricity use can be gradually increased to cannot accept a power outage, to the power grid voltage waveform, harmonic, frequency and so on each index has a clear requirement, countries have introduced related national standard specification. This aspect shows a gradually increase in the standardization of industrialization in China, on the other hand also puts forward higher demands on distribution power supply departments. One of the goals of the grid is to reduce the failure rate, improve the reliability of power supply. And power grid failure and is the highest percentage of short circuit fault, and the single-phase earth fault and occupy the vast majority of proportion, it is necessary to in-depth analysis of the formation mechanism of single-phase earth fault and damage, so as to find the related protection and remedial measures.

Optimization of Distribution Network Voltage Deviation Control Measures Based on LCC Theory

This paper presents an optimization method for distribution network voltage deviation control measures based on life cycle cost (LCC) theory. The overall process of this method had been introduced, and the LCC decomposition model of distribution network reconstruction had been analyzed in details. The existing statistical data were used to build the LCC cost models for changing the cross section of the conductor, reactive power compensation and adjusting the transformer tap. Finally, a 10kV feeder was taken as an example to illustrate the application of the proposed method, it included the voltage deviation estimation, voltage improvement plan development and plan optimization based on LCC theory. The method proposed in this paper can be used to improve scientificalness and economical efficiency in planning distribution network voltage deviation control measures by power supply department.

Integration Research of Thermoelectric Enterprise Marketing Management Information System

Due to business expansion and increased market competition, Tianfu thermoelectric original electric gas marketing system can no longer meet the needs of enterprise development which lead to information islands. In response to these issues, based on the business needs, this paper constructed a marketing network management system platform integrated with electricity, heat and gas for the thermal power enterprises. This system includes: 1) Establishment of enterprise data center and centralized storage of the business system data. 2) Establishment of enterprise portal system for achievement of single sign-on. 3) Establishment of power energy information collection and management system of the power purchase, supply and sale departments. 4) Establishment of "all-in-one-card" system including electricity, heat and gas IC card. 5) Creation of a marketing analysis and strategic decision-making system. The system platform can offer efficient and reliable basis for business processing capabilities to electricity, heat, and gas marketing business.