Table of Content

28 June 2023, Volume 56 Issue 6

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Special Contribution

Potential and Utilization Scenarios for Regulation Resources Across Energy Varieties in China

WANG Linyu, ZHANG Fuqiang, GONG Yichun, XIA Peng, SHI Wenbo, PAN Hangping

2023, 56(6):  1-10.  DOI: 10.11930/j.issn.1004-9649.202302078

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The construction of the regulation system is of importance for the construction of a new power system, the traditional path faces challenges such as shortage of resources, high costs, and lack of long-term means. this paper firstly defines the research object and scope of adjustable resources across energy varieties, and then analyzes the demand for regulation resources in different periods and the potential of adjustable resources across energy varieties, and conducts a cost-benefit analysis by scenario analysis. The results show that the development and utilization of adjustable resources across energy varieties has economic, social and environmental values, and can play a key role and become an important supplement in the construction of the regulation system. This study can provide a reference for further enriching the sources of regulation resources, reducing regulation costs, and carrying out business model research.

Stability Analysis and Control of New Energy Power System

Frequency Modulation and Rotor Speed Recovery Strategy of Doubly-Fed Induction Generator Based on Model Predictive Control

ZHAO Jingjing, DU Ming, LIU Shuai, LI Zibo, MA Wenhe

2023, 56(6):  11-17.  DOI: 10.11930/j.issn.1004-9649.202210120

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The doubly-fed induction generator (DFIG) can participate in the system frequency modulation by releasing the kinetic energy stored in the rotor, but it may cause a secondary frequency drop when the speed is recovered, which is adverse to the frequency stability of the system. Therefore, a strategy for restoring the speed of the rotor in the inertia control of DFIG based on model predictive control is presented in this paper. Firstly, based on the influence of the active power reduction on the system frequency and the rotor speed in the inertia control of DFIG when the speed is recovered, the predictive control model is established; secondly, the objective function considering the reduction of the secondary frequency drop of the system and the recovery of the rotor speed is formulated, and the active power reduction in line with the system frequency in real time is optimized, so as to suppress secondary frequency drop, ensure rotor speed recovery, and improve the stability of the system frequency; lastly, the simulation model is established on the Matlab/Simulink to verify the effectiveness of the proposed control strategy.

Analysis of Sub-synchronous Torsional Vibration of Wind-Thermal Bundling Transmission System via LCC-HVDC

ZHAO Yue, YAN Gangui, WANG Zhenyang, REN Shuang, WANG Dazhong, GUO Jianyu

2023, 56(6):  18-30.  DOI: 10.11930/j.issn.1004-9649.202302060

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In recent years, wind energy resources are strongly developed, and wind-fire bundling via direct current (DC) transmission has become the main method of current and future development and utilization in China. The influence law of permanent magnet synchronous generator (PMSG) cluster and high voltage direct current (HVDC) transmission on the electrical damping of various torsional vibration modes of shaft system of synchronous generators (SGs) needs to be further studied. In this paper, the linear model of the PMSG cluster, thermal power unit, and HVDC system is established. The interactive coupling relationship between access equipment and thermal power unit’s electrical damping and the mechanism of torsional vibration of the shaft system of the thermal power unit caused by wind-fire bundling via DC transmission system are analyzed. Based on the complex torque coefficient method, the influence of PMSG operating wind speed, control parameters, DC transmission power, and other factors on the electrical damping coefficient of SGs is studied. Finally, a time-domain simulation model for wind-fire bundling via DC transmission system is built in PSCAD/EMTDC, and the validity of the analysis results of the electrical damping coefficient is verified by time-domain simulation and time-frequency analysis.

Identification Method for Control Parameters of Doubly-Fed Induction Generator Based on LSTM Neural Network

XUE Fei, LI Hongqiang, LI Xutao, XU Hengshan

2023, 56(6):  31-39.  DOI: 10.11930/j.issn.1004-9649.202208091

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Since it is difficult to obtain highly accurate control parameters of the electromagnetic model of a doubly-fed induction generator (DFIG) under transient conditions, a high precision identification method for the control parameters of DFIG based on long short-term memory (LSTM) neural network was proposed. Firstly, the RT-LAB hardware-in-the-loop (HIL) simulation platform was used to measure and obtain the HIL data of the DFIG controller, and the identification model of DFIG was built in the Plecs platform. Secondly, the Person correlation coefficient method was used to extract highly correlated features and train the neural network. Finally, the proposed LSTM neural network was used to identify the control parameters of DFIG and compare them with the measured data. As a result, the feasibility, effectiveness, and practicability of the proposed method were verified. The results show that compared with the traditional identification methods, the proposed parameter identification method based on LSTM neural network can effectively improve the identification accuracy of the control parameters of the electromagnetic model of DFIGs under transient conditions.

Coordinative Optimization of Emergency Control Based on Constraint Consensus and Differential Evolution in Multi-DC Infeed Power Grid

ZHUANG Jun, WANG Deshun, GAI Chenhao, XUE Jinhua, ZHU Hongbao, LI Changgang

2023, 56(6):  40-50.  DOI: 10.11930/j.issn.1004-9649.202302012

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The multi-direct current (DC) infeed power grid faces the risk of system instability after the DC blocking fault. Coordinating multiple emergency control resources can ensure system stability and control economy. The coordinative optimization model of emergency control is constructed by considering three measures: DC power modulation, pump-storage shedding, and load shedding. In order to meet the practical engineering requirements of quickly obtaining a feasible control scheme, an optimization method based on constraint consensus and differential evolution is proposed to solve the optimization model. First, since it is hard to guarantee the feasibility of the emergency control scheme through random initialization, the constraint consensus method is used to construct the feasibility direction vector. The vector guides the control scheme to quickly transform into a feasible scheme along the gradient direction of constraint violation. Then, based on the generated feasible control scheme, a differential evolution optimization strategy is designed by using the direction information of the feasible region, so as to explore and develop the feasible region and improve the economy of the scheme. Finally, the effectiveness of the proposed coordinative optimization method of emergency control is verified by simulating a provincial power grid.

MPC-VSG Based Control Strategy for Dynamic Stability of Frequency and Voltage in Islanded Microgrid

SUN Jiahang, WANG Xiaohua, HUANG Jingguang, CAO Hao, MEI Nuonan, LI Zhedong

2023, 56(6):  51-60,81.  DOI: 10.11930/j.issn.1004-9649.202302058

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In a high proportion of new energy islanded microgrids, the reduction in the proportion of conventional synchronous generator (SG) will lead to a gradual decrease in their overall inertia. The conventional control strategy cannot effectively coregulate the frequency and voltage in microgrid under low inertia conditions. To simultaneously improve the dynamic stability of the frequency and voltage in the islanded microgrid, this paper proposes a model predictive control (MPC) based frequency-voltage control strategy for energy storage virtual synchronous generator (VSG). By developing a predictive model of VSG, a cost function of frequency and power is designed, and the power reference value of VSG is dynamically corrected after calculating the required active and reactive power increments. Considering that the interaction between SG and VSG reduces the transient voltage stability of the system, the voltage control loop is improved by reducing the angular difference between SG and VSG during transients, and the reactive power reference value is further adjusted. The dynamic stability control of the system frequency and voltage is achieved based on the power reference value. Finally, the simulation results verify the effectiveness of the strategy and the superiority.

Power System

Energy Storage Regulation Method of Base Stations in 5G Integrated Distribution Network Based on Energy Sharing and Trading Coordination

WANG Yanru, YIN Xiyang, OU Qinghai, MA Wenjie, LIU Hui, DU Zhigang

2023, 56(6):  61-70.  DOI: 10.11930/j.issn.1004-9649.202207038

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With the rapid development of 5G technology, the large-scale application of 5G base stations with high energy consumption increases the operation costs of base stations and exacerbates problems such as the supply-demand imbalance in the distribution network. To this end, this paper analyzes the regulation potential of 5G base stations based on their energy consumption characteristics and constructs the energy sharing model among base stations and the energy trading model between base stations and distribution network by combining the dynamic changes of energy consumption and photovoltaic output of 5G base stations. Then, with the optimization objectives of promoting energy sharing of 5G base stations, improving operation efficiency of base stations, and participating in peak shaving and valley filling, the optimization problem of energy storage regulation of base stations in a 5G integrated distribution network is constructed, and an energy storage regulation algorithm of 5G base stations based on energy sharing and trading coordination is proposed. Finally, simulation results show that the proposed algorithm can improve the operation efficiency of base stations, promote peak shaving and valley filling in the distribution network, and effectively consume photovoltaic output.

A Comprehensive Evaluation and Prediction Method for Load Density Based on Big Data under Power Supply Partition Scenarios

JIA Wei, LEI Caijia, FANG Binghua, LIU Yong

2023, 56(6):  71-81.  DOI: 10.11930/j.issn.1004-9649.202205025

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In order to meet the requirements of power supply partition and grid planning, a comprehensive evaluation and mid-long term refined prediction method for load density based on big data under power supply scenarios is proposed, and similar units are clustered through the improved Agglomerative algorithm. The proposed method can effectively extract the typical features of various load densities, so as to reduce the requirement of the system for data sampling and provide support for the classified refined forecasting of various loads. Firstly, based on the data samples, the load density features of the plot samples in the grid are extracted with the kernel density estimation (KDE) method. Then, the entropy method is used to weight the eigenvalues to realize the evaluation of different types of load densities in each power supply unit, and further calculate the integrated load density level of the power supply units and power grids. Finally, the power supply units are clustered, and the parameters of the S-shaped growth curve are solved by the least square method, so as to realize the mid-long term prediction of various load densities. In case study, a detailed analysis is carried out, and the effectiveness of the method is verified by engineering examples.

Effect and Mechanism of Salt Spray on Electrical Insulation Properties of Silicone Rubber for Cable Accessories

WANG Jingbing, ZHANG Fan, CHENG Zhaolu, WANG Jiaxing, ZHOU Xuguang, QI Pengshuai, WEI Yanhui, LI Guochang

2023, 56(6):  82-89,100.  DOI: 10.11930/j.issn.1004-9649.202211091

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Salt spray is an unavoidable environmental factor for the operation of offshore power equipment, which can easily cause discharge faults such as flashover and breakdown of cable accessories. In this paper, a study was conducted on the variation features of electrical insulation properties of silicone rubber materials after salt spray treatment in terms of microstructure, hydrophobic properties, dielectric properties, surface properties and discharge characteristics, and the influence mechanism of salt spray on insulation properties of silicone rubber materials was explored. The experimental results show that the hydrophobic properties of silicone rubber gradually decrease with the aging time. The penetration of salt spray solution into the silicone rubber surface leads to the increase of water molecules and impurity ions, consequently resulting in the decrease of the surface resistivity and the rise of the surface traps. Besides, both of the flashover voltage and the breakdown strength decrease to a certain extent, owing to the corrosion of Cl^–1, which will destroy the molecular chain of silicone rubber samples. This study has guiding significance for evaluation of the aging performance and failure of cable accessories.

Optimal Scheduling of Honeycomb Distribution Network Based on BADMM

ZHU Pengcheng, LIU Zhaoyu, SUN Ke, XU Jie, HU Pengfei, JIANG Daozhuo

2023, 56(6):  90-100.  DOI: 10.11930/j.issn.1004-9649.202208017

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The honeycomb active distribution network is a new type of distribution network structure for large-scale microgrid clusters, which can realize efficient integration of large-scale distributed renewable energy. Aiming at the complex problem of the coordination operation between microgrids and smart power/information exchange stations (SPIES) in the honeycomb distribution network, this paper proposes a distributed optimal scheduling strategy based on block-wise alternating direction method of multipliers (BADMM) to minimize the operating cost. Firstly, a honeycomb distribution network optimization mathematical model is established according to the topology. And then the honeycomb distribution network is divided into multiple areas with SPIES as the center, and each SPIES coordinates and synchronously calculates the optimization problem of its adjacent microgrids, realizing the global optimization. Finally, the effectiveness and convergence of the proposed optimal scheduling strategy is verified by an example.

Robust State Estimation Method for Electric-Gas-Heat Integrated Energy System Considering Boundary Equation Constraints

GUO Mengfang, DU Xiang, WANG Fei

2023, 56(6):  101-106.  DOI: 10.11930/j.issn.1004-9649.202204043

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A measurement state equation was established for the integrated energy system (IES) of electricity gas heat under non Gaussian measurement noise. Considering the boundary conditions of coupled components, which serve as both equality constraints and virtual measurement equations, a robust state estimation method for electrical pneumatic thermal IES considering boundary equality constraints is proposed based on the maximum likelihood robust state estimation method. Conduct simulation verification under Gaussian and non Gaussian noise. The simulation results demonstrate that the proposed method can provide accurate global state awareness for online analysis and optimization scheduling of IES.

New Energy

Optimization Performance and Efficiency Improvement of Microgrid Scheduling Model

JIA Honggang, WANG Zhuding, YUE Yuanyuan, YAN Huan, YAN Na, CAO Qiangfei

2023, 56(6):  107-113,131.  DOI: 10.11930/j.issn.1004-9649.202212028

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For the optimal management of microgrid, this paper proposes a microgrid optimal scheduling strategy based on adaptive hybrid differential evolution algorithm. First of all, considering the cost of electricity charges, the cost of energy storage regulation, the user’s inappropriate cost and the benefit of response subsidies, an optimization model with the goal of minimizing the daily comprehensive power consumption cost is established; Then, aiming at the problem that the global optimization ability of the standard differential evolution algorithm is insufficient and the solution efficiency needs to be improved, the scaling factor and crossover operator are optimized and improved to form an adaptive hybrid differential evolution algorithm; Finally, taking the micro grid system as an example, the operation optimization of energy storage and demand response and the comparative analysis of calculation examples are carried out.The results show that the proposed method is suitable for multi scenario computing and has high optimization performance and solving efficiency.

Optimization Strategy of Building Energy System Based on Deep Reinforcement Learning

SHI Wenzhe, LI Bingjie, YOU Peipei, ZHANG Ling

2023, 56(6):  114-122.  DOI: 10.11930/j.issn.1004-9649.202210043

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Aiming at the load uncertainty on the demand side of the building energy system and the randomness of renewable energy on the supply side, a building energy system management optimization strategy is proposed based on deep reinforcement learning. Firstly, a supply-demand side research framework for the energy system and device model is built. The building energy management problem under the real-time stage is constructed as Markov decision-making process, and the deep reinforcement learning theory is used to minimize the cost of electricity, ensure the indoor heat comfort level and maximize the consumption of renewable energy as the optimization goals, and the duel dual deep Q network algorithm is used for model training, and the trained model can make adaptive control decisions according to real-time environmental parameters. Finally, through the application in the building energy system case, the results show that the proposed optimization strategy reduces the cost of electricity by 11.03%, the duration of thermal discomfort by 89.62%, and the amount of unconsumed photovoltaic power generation by 10.43%, comparing with the traditional rule-based control strategy.

Temperature Distribution in Planer Solid Oxide Fuel Cell

ZHANG Xiaokun, LV Dawei, YIN Zhongqiang, SHEN Shuanglin, WANG Shaorong

2023, 56(6):  123-131.  DOI: 10.11930/j.issn.1004-9649.202209095

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Thermal stress induced by temperature gradient in solid oxide fuel cell (SOFC) is one of the key problems restricting its life. In view of the influence of the electric furnace on the experimental results in the traditional SOFC temperature distribution measurement experiment, this paper proposes a method of insulating the test cell to provide an approximately adiabatic working environment for the cell, so as to expand the experimental results to the actual stack. Using this method, the temperature distribution law in the flat SOFC single cell was experimentally studied. The experimental results show that the cell insulation can effectively reduce the heat exchange between the cell and the electric furnace. At 24 A discharge, the maximum temperature in the cell is 782 ℃, 32 ℃ higher than that of the furnace, which proves that cell insulation can effectively reduce the influence of the electric furnace; when the discharge current is set at 18 A, 24 A and 30 A, the maximum temperature in the cell is 777 ℃, 782 ℃ and 796 ℃ respectively, but all the maximum temperature differences are about 5 ℃; the temperature gradient in the cell is greatly affected by the cooling effect of the intake air, but its influence range is small. Therefore, a large temperature difference will be generated locally at the gas inlet of the cell, which will affect the operational safety of the cell.

Capacity Planning and Operation Strategy of New PV-Storage Power Station Based on Frequency Modulation Service

QIAN Guoming, MENG Jie, ZHU Haidong, DING Quan, CHEN Xiaoyu

2023, 56(6):  132-138,147.  DOI: 10.11930/j.issn.1004-9649.202210083

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In order to take full advantages of the new PV-Storage power station participating in power grid multi-time scale frequency modulation while considering the operation economy. First of all, according to the characteristics of photovoltaic modules, flywheel energy storage and lithium iron phosphate energy storage, their life cycle models are established respectively, and a coordinated operation strategy is proposed to reduce energy storage battery attenuation and improve frequency modulation performance. Second, on the basis of the primary and secondary frequency modulation mechanism, the model of PV-Storage power station participating in power grid frequency modulation capacity planning is established with the maximization of the frequency modulation revenue as the optimization objective function. Finally, a simulation model of PV-Storage system is built by virtue of Matlab software. The simulation results show that, as it is guaranteed that the requirements are fully met for second-level primary frequency modulation and minute-level secondary frequency modulation, the hybrid energy storage combined with photovoltaic reserve capacity can make resource planning more reasonable and improve both the economy and reliability of frequency modulation. Moreover, the reduction of flywheel cost will allow more capacity allocation so as to increase the frequency modulation income, hence the investment return period can be shortened.

Optimal Scheduling of Carbon Capture Power Plants Based on Integrated Coordinated Energy Storage System under the Background of Carbon Trading

FENG Shuai, YUAN Zhi, LI Ji, WANG Weiqing, HE Shan

2023, 56(6):  139-147.  DOI: 10.11930/j.issn.1004-9649.202209068

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In the process of coal-fired power low-carbon transformation, new challenges emerge such as the weakening performance of carbon capture level during peak load period, the deviation between carbon quota calculation and actual application, etc. In view of these issues, the paper first introduces hydrogen fuel cells and hydrogen storage devices to formulate a comprehensive coordinated energy storage system, which can improve the insufficient carbon capture level during peak load period of carbon capture power plants; Then, the carbon quota calculation model is established, such that the calculation method of carbon quotas is more in line with practical applications; Finally, by setting the minimization of comprehensive operating cost of the system as the objective function, the optimal scheduling model of carbon capture power plants based on the comprehensive coordinated energy storage system is constructed, and the proposed model is solved using Cplex software package. The calculation results show that the carbon capture power plant can improve the carbon capture level and economy while prioritizing wind power consumption, by taking into account the low-carbon nature of the system.

Multi-objective Cooperative Optimization of Multi-heterogeneous Energy System Considering Energy Conservation

CHEN Quan, ZONG Xuanjun, LI Mengyuan

2023, 56(6):  148-157.  DOI: 10.11930/j.issn.1004-9649.202211022

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The planning and operation of multi-heterogeneous energy systems often only considers a single optimization objective, which is not conducive to the balanced among economic operation, low carbon emission and energy conservation. This paper proposes a multi-dimensional objective collaborative optimization method that considers energy saving. Firstly, a multi-dimensional optimization objective is proposed with consideration of system economy, environmental protection and energy saving, and a multi-objective optimization model framework for multi-heterogeneous energy system is established by modeling energy conversion and distribution equipment in the system. Then, the NSGA-Ⅲ (non-dominated sorting genetic algorithm Ⅲ) is utilized to solve the multi-objective collaborative optimization model proposed in this paper, and the optimal feasible solution set (Pareto optimal solution set) is obtained. Finally, based on the simulation example, the effectiveness of the proposed method is verified through analysis of the optimal solution set and decision-maker preference. It is also demonstrated that considering the objective of energy conservation can avoid the system's excessive preference to consuming natural gas energy.

State Recognition of Wind Turbines Based on K-means and BPNN

YANG Xiaofeng, FANG Yihang, ZHAO Pengzhen, WANG Chengmin, XIE Ning

2023, 56(6):  158-166,175.  DOI: 10.11930/j.issn.1004-9649.202203070

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In order to achieve the goal of “double carbon”, the development of wind power generation technology is essential. At the same time, with the increasing complexity of power grid, the real-time detection and accurate evaluation of the state of wind turbines and other power equipment are becoming increasingly important. In recent years, the development of big data technology and the improvement of power equipment data monitoring technology makes possible the application of big data technology in power equipment state recognition. Compared with the conventional methods, the above-mentioned methods are independent of accurate empirical thresholds or quantitative models, and have better adaptability to the rapid increase and variability of data. Thus, this paper applies the unsupervised (K-means) and supervised (BPNN) machine learning methods to state recognition of wind turbines, while exploring the variation of accuracy and computational efficiency after the application of dimensionality reduction methods. The results show that both machine learning methods are effective in state recognition of wind turbines, while the dimensionality reduction method can effectively improve the computational efficiency with limited accuracy loss.

Path Optimization of Submarine Cables for Offshore Wind Farm Considering Feeder Crossing Avoidance

YE Jing, ZHOU Guanghao, ZHANG Lei, YANG Li, ZHAI Xue, CAI Junwen

2023, 56(6):  167-175.  DOI: 10.11930/j.issn.1004-9649.202201026

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The topology optimization of power collection in offshore wind farms is a large-scale non-convex and nonlinear optimization problem, for which it is difficult to obtain the optimal solution. It is therefore divided into two parts: internal topology optimization and feeder path optimization. Inside the partition, the optimal topology mode is obtained by taking economy as the objective function with consideration of submarine cable selection. In the feeder part, aiming at the submarine cable crossing problem, a partition regularization scheme is proposed with consideration of the actual construction engineering constraints of the wind turbines. By using the improved Dijkstra algorithm, the transition from the traditional line-line intersection judgment to the line-surface intersection judgment is realized, and the situation that the crossover experiment cannot judge the submarine cable crossing is avoided. Finally, a case study of two offshore wind farms is studied and the proposed algorithm is compared with other topological results, which has proved the feasibility and superiority of the proposed algorithm.

Technology and Economics

An Optimization Coal Procurement and Inventory Model for Power Generation Enterprises Based on Data-driven Chance Constraints

YAO Li, ZHENG Haifeng, SHAN Baoguo, TAN Xiandong, XU Chuanlong, XU Zhicheng

2023, 56(6):  176-184.  DOI: 10.11930/j.issn.1004-9649.202210036

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Optimization of coal procurement and inventory for power generation enterprises are of great significance for guaranteeing power supply and ensuring generation income. The requirements for safe coal inventory level have been clearly put forward by the energy administrative authority of our country. However, no existing research has ever focused on the probabilistic model and corresponding optimization strategy for the violation risk of inventory caused by the uncertainties of power generation and transportation capacity. Aiming at this problem, this paper presents an optimization coal procurement and inventory model for power generation enterprises based on data-driven chance constraints and proposes a corresponding solution method. Firstly, with consideration of the uncertainty of power generation and transportation capacity, the data-driven chance constraints for inventory are established and converted to soluble constraints of conditional value at risk (CVaR). Furthermore, based on the convexity of CVaR to decision variables, a piecewise linear approximation method for CVaR constraints is proposed. A power generation enterprise which owns 10 coal power plants is selected for case study. The optimization results show that with consideration of the chance constraints, the violation risk of power coal inventory is restricted within the allowable range; the proposed piecewise linear approximation method for CVaR constraints can make the model scalable and reduce the model’s scale with a high accuracy.

Effective Asset Operation Efficiency Assessment of Power Grid Enterprises in Context of New Power System

YE Yingjin, LIN Ling, RUAN Di, ZHANG Shiming, LIN Hongyang

2023, 56(6):  185-193.  DOI: 10.11930/j.issn.1004-9649.202209033

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The accelerated construction of new power system brings challenges to the asset management of power grid enterprises, and the accurate evaluation of asset operation efficiency is the first condition to improve the asset management level of power grid enterprises. Firstly, this paper analyzes the impact of the new power system on the effective asset operation of power grid enterprises, and constructs an input-output index system for the evaluation of the effective asset operation efficiency of power grid enterprises. Secondly, considering that the uncertainty of new energy generation and electricity price subsidy policy will make it difficult for statistical data to accurately reflect the real situation of input-output efficiency of power grid enterprises, this paper modifies the traditional DEA model based on the robust optimization idea and dual theory, and constructs a robust DEA model that takes into account the unexpected output and input-output uncertainty. Finally, 25 power grid enterprises are selected for model application. The results show that the efficiency ranking accuracy of the robust DEA model is 60 percentage points higher than that of the traditional DEA model, which can effectively deal with uncertain environments, achieve the accurate calculation of the effective asset operation efficiency of power grid enterprises in the context of new power system, and provide a decision-making basis for asset management of power grid enterprises.

Information and Communication

Ultrasonic Detection Sensor of Digital Distribution Network Based on EFPI

JU Ling, HUANG Yi

2023, 56(6):  194-201.  DOI: 10.11930/j.issn.1004-9649.202207022

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In order to meet the demand of partial discharge ultrasonic signal detection of digital distribution network equipment, an ultrasonic sensor based on extrinsic Fabry Perot interferometric (EFPI) is developed to detect the ultrasonic signal generated by partial discharge. Through the simulation software COMSOL, the sensitivity and natural frequency of the optical fiber Fabry Perot (F-P) sensors with different diaphragm thicknesses and diaphragm radius are simulated. And the EFPI sensors are fabricated with the selected optimal structural parameters. The simulation results show that the sensor can reach 106.6 nm/kPa in sensitivity and 234.85 kHz in natural frequency. When the ultrasonic signals with a frequency of 30 kHz and 40 kHz are added to the sensor respectively, the corresponding frequencies detected by EFPI ultrasonic sensor are 29.97 kHz and 39.92 kHz. The experimental results show that the sensor, which has the characteristics of small size, wide frequency response range and high detection sensitivity, can accurately detect the ultrasonic signal generated by local discharge in the high-voltage simulating tank with a minimum detectable discharge of 53 pC, and will have good application prospects.

Substation Data Compression and Storage Method Based on Improved Revolving Door Algorithm

YU Yang, ZHANG Hao, WANG Tongwen, WANG Wei, BIAN Ruien, LUO Nianhua

2023, 56(6):  202-208.  DOI: 10.11930/j.issn.1004-9649.202203116

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In order to solve the data storage problem caused by the access of the substation supervisory control and data acquisition (SCADA) system to the data storage problem, a substation data compression and storage method based on the improved revolving door algorithm is proposed. Firstly, the lossy compression algorithm of the revolving gate is introduced, and in view of the shortcomings of fixed storage frequency, fixed threshold value and ignored abnormal points, the adaptive frequency conversion data storage strategy, the dynamic adjustment threshold value strategy and the abnormal point recording strategy are proposed to improve the accuracy of the algorithm. Secondly, the variable position storage method is adopted for the remote message, remote control and remote adjustment data of the SCADA system of the substation, and the improved revolving door algorithm is adopted for the telemetry data. Finally, the effectiveness of the proposed method was verified through numerical examples.

Decoupled Sematic Distance Based Multi-class Defect Scene Detecting for Substations

ZHANG Xin, YE Junjie, CUI Yao, HUANG Xin, ZHONG Linlin

2023, 56(6):  209-218.  DOI: 10.11930/j.issn.1004-9649.202208117

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Due to the complexity and differences of defect types in substations, traditional deep learning models for defects detection lack comprehensive response ability. It proposes a sematic distance based decoupling detection model. Firstly, the decoupled model structure is determined by clustering defect classes according to the semantic information distance between each other. Then, the weighted anchor fusion and local prediction loss techniques are used to improve the model performance. Meanwhile, the decoupled non-maximum suppression strategy is proposed to accelerate the model inference process. The experiment results show that the mean average precision of the model reaches 69.68%. Compared with YOLOX, which has been recognized as the best real-time object detection model, the accuracy of proposed model is improved by 1.36 percentage points, the parameter quantity is reduced by 5%, and the inference speed is improved by 34%.