Table of contents

The 3^rd International Conference on Energy, Electrical, and Power Engineering (CEEPE) was planned to be held in April 24-26, 2020, in Chongqing, China, and was co-sponsored by the University of Electronic Science and Technology of China; Wuhan University, China; Shandong University, China; South Asia Institute of Science and Engineering (SAISE) and IEEE PES Chongqing Chapter.

Due to the unexpected COVID 19 pandemic, that closed the world to national and international travel, the organization decided to hold the CEEPE 2020 totally online. It was a challenge, not only to the organizers, but also to the participants who were delivering their speeches using videoconference tools, under different time zones.

This conference brought together researchers and engineers in academia and industry from China, Iraq, Japan and Portugal to discuss the latest developments and implementation, in these countries, of electrical power in applications ranging from commercial generation and distribution, to electric vehicles, with a particular focus on renewable energy integration in power systems, automation and control systems.

Although there was not the opportunity to meet face-to-face under a live social environment, attendees from different areas of the world could interact under the videoconference tool, clarify their doubts and establish business and technical connections. The proceedings of these presentations are listed here in peer reviewed, written form.

Nídia Caetano

Coordinator Professor of Chemical Engineering

School of Engineering (ISEP)

Polytechnic of Porto (P.Porto)

List of International Advisory Committees, Conference Committee Chairs, Program Committee Chairs, Local Chair, Steering Committee and International Technical Committees are available in this pdf.

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the proceedings Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

In this paper, a novel quasi-proportional resonance (quasi-PR) controller for grid-connected current control is proposed. For the traditional quasi-PR controller, the performance decreases in cases when a large gird frequency deviation is going through. To solve the problem, a frequency adaptive structure is applied in the current control method to achieve online correction on the resonance frequency, so that a high control accuracy is maintained and the robustness of the control system is enhanced. The feasibility of the proposed method has been verified by experimental results.

The output power of electric-field energy harvesting is limited by displacement current produced in the capacitive coupling between the harvester and power line. This paper presents a capacitive electric-field energy harvester (CEFEH) that obtains energy from 220V power line by displacement current. The CEFEH consists of copper foil wound around the power line and ground electrode. In order to improve the output power of CEFEH, a double-layer copper foil is used to enhance the capacitive coupling, thereby increasing the displacement current. With the help of larger displacement current, the harvesting power of CEFEH with double-layer copper foil is 1.6 times higher than that of the single-layer copper foil. However, the electric-field energy around the 220V power line is very weak, which makes wireless sensor node unable to work directly. A power management circuit is designed for CEFEH. The test results show that the proposed CEFEH collects 2.468mJ in 400s and drives the ultra-low power wireless temperature sensor node to operation for 800ms. This CEFEH can be used in wireless sensor node with a short-term ultra-low power.

Under the effect of lightning current which is with large amplitude, the grounding device and surrounding soil will have spark effect, which makes device show complex impact characteristics and affect the current dissipation process. In this paper, using the parallel plate device, the quantitative relationship between the resistivity of sandy and brown yellow soil and the field strength is obtained, which is expressed as a piecewise function. Considering this relationship, a finite element model of impact dissipation for grounding device is established. A test platform is built, and the impact test data of different topological forms is used to compare with the results of the model, which can verify that the model can effectively analyse the spark effect of the grounding body. After carrying out simulation for the grounding body of different topological forms, this paper analyses the impact dissipation characteristics of the horizontal grounding body and the grounding body with short conductors.

In the past few years, the number of large-scale photovoltaic (PV) power station is increasing quickly. Due to the power fluctuation of PV station, the voltage in the power grid with large-scale PV stations integrated may fluctuate irregularly. It may cause transformer tap or shunt capacitor switching frequently. To smooth the voltage fluctuation, a typical solution is to install energy storage system which needs high investment. The alternative way is to use reactive power to regulate the voltage. This paper introduces the ability of reactive power control to mitigate the above voltage fluctuation problem. An improved ΔQ(ΔP) control is proposed. Comparing with the common Q(U) droop control, the results of an actual PV station case verifies its good performance.

Though the silicide metallized electrodes which are widely used in semiconductor devices have their advantage of low contact resistance, they often induce harmful stress in devices. Considering this, a trench-based structure designed to manipulate the silicide induced stress is reported in this letter. The effects of the structure are demonstrated by the numerical simulation results based on the typical PMOSFETs with their electrodes formed by CoSi2 and NiSi. It is shown that, while the trench-based structure is applied, the harmful tensile channel stress caused by the silicide electrode can be reduced as low as about 50%. As a result, the output current can be increased about 7.5%. These results indicate the potential of this novel trench based structure to serve as the stress modulation structure in the semiconductor device with silicide electrodes.

When the lightning strikes the tower, the lightning current is discharged into the ground through the tower grounding device. The magnitude of the impulse earthed resistance of the tower grounding device determines the lightning discharge capacity of the tower. Therefore, the tower grounding device directly affects the lightning protection level of the tower. This paper first introduces three commonly used shock resistance reduction methods: long vertical ground electrode, acupuncture ground electrode, and externally induced rays. Then, the CDEGS software is used to simulate and analyze the long vertical grounding electrode, and a method of insulation short shield is proposed to determine the optimal length of the vertical grounding conductor of the grounding device. Finally, the different resistance reduction effects under the change of length and number of roots are explored, and the optimal resistance reduction arrangement under the same economic conditions is realized.

Aiming at the radar waveform design problem of detecting multiple targets in the presence of clutter, a novel algorithm based on linear probability-weighted summation (PWS) of multi-target impulse response of signal to interference plus noise ratio (SINR) is proposed. In view of the traditional water-filling algorithm, the multi-target detection algorithm is further studied in a new way to improve the overall performance of the system. This method uses Jensen's inequality to make a lot of derivations to determine the target function of the algorithm and the constraints. Simulation results show that the proposed algorithm has better detection performance and more accurate target information.

The DC de-icer installation has been widely used in the ice protection of power system. Because of the difference between the design and the actual operation of the installation, the resonance overvoltage has been threatening the device body from the distribution parameters of the de-icer transmissions. In this paper, based on the analysis on the mechanism of the resonance overvoltage, the estimate expression of the resonant length of the transmission has been derived. Through the simulation modeling by PSCAD/EMTDC, the effectiveness of the estimate expression has been verified. The suppression measures of the resonance overvoltage have been proposed.

This paper describes a comparison between the specific DC railway models with insulted gate bipolar transistors rectifiers and diode rectifiers. As silicon diode was applied to railway electrification system decades ago, the IGBTs rectifiers have not obtained wide application yet. In this research, the performance of these rectifiers is presented and analysed. The power supply to the load, system losses and effects to systems are the main aspects compared in this paper. The analysis presents the possibility that new types of power converters can be adapted in DC railway electrification system. The simulations are carried out with MATLAB and Simulink.

In this paper an accurate method for efficiency calculation of self-excited induction generator (SEIG) at different operating conditions is proposed, considering the effect of stator and rotor iron core losses, stator and rotor circuits stray load losses, stator and rotor winding (copper) losses, mechanical losses, skin-effect losses, and skew-effect losses. The proposed procedure of efficiency calculation is based on the conventional machine tests such as no-load test, load test at variable supply voltage and temperature test without additional measurements. From these conventional tests, all the SEIG losses can be calculated accurately by their related formulas derived based on the SEIG equivalent circuit. The reduction of these losses causes an increase in the SEIG percentage efficiency, and this is a very important feature in electric energy saving by minimization of SEIG equivalent circuit components.

This paper present an integral sliding-mode controller based on equivalent control to overcome the disadvantages of non-minimum phase system, which the z-source inverter has when it is used in the motor drive system of electric vehicles. Based on the pseudo-output, which is defined by measured capacitor voltage and inductor current on z-source network, a constant capacitor voltage control strategy for z-source inverter is given. With the reaching condition imposed, the controller can guarantee the stability of closed-loop system and the robustness of system output. The simulation results show that the closed-loop control system for z-source inverter can achieve better global robustness with zero steady error, and that the capacitor voltage on z-source network can be stabilized with good performances of motor drive system.

As the core of industrial control, servo drives have become evident in small volume, multi-spindle integration, acclimatization and energy conservation in recent years. The main domestic and foreign servo drives adopt module temperature detection, thermal accumulation - heat dissipation model or inverse time overload algorithm. The overload multiple and time are relatively fixed, and cannot further improve overload capacity according to the environment temperature or load condition. Due to the low utilization rate of the radiator, the servo drive selection usually amplifies the first gear and increases the cost of the electrical system. Without adding energy feedback cells or capacitors, it's not efficient to recycle the energy produced by braking. This paper mainly studies the overload algorithm which based on junction temperature prediction. The overloading algorithm can increase the overload capacity by 40%. Fanless design can be realized to meet the application of negative conditions such as conductive dust, cutting fluid and cotton. In general, the overload algorithm which based on junction temperature prediction is suit for the application of printing, packaging, semiconductor silicon, forging, laser cutting machines.

This test procedure describes the evaluation method used to determine the water consumption performance of cooling towers and evaporative cooling equipment, which provides a unified test instrument, test procedure, parameter measurement, test data processing, and test results. This test procedure provides the manufacturer and the owner an objective and fair evaluation, outlines practical methods for monitoring the water consumption performance of cooling towers. The loss of water in cooling tower is measured by U-type liquidometer, and the law of collecting basin liquid level. When the liquid level of the collecting basin decreased by 1 mm, the water quantity loss of one cooling tower diameter D=42 m was 1.38 m³. And cooling tower water loss and specification for water balance test is established. The water quantity loss of cooling tower can be obtained conveniently, accurately and quickly. This provides conditions for further accurate analysis of water quantity loss of cooling tower, etc. It is widely used and has great significance in the hydraulic design of cooling tower, the analysis of air status in the tower, the water balance test to determine the water used in cooling tower, the reduction of water loss and the drift recovery. And provides explicit test procedures that yield results with the highest level of accuracy and consistent with the best current engineering practices and knowledge in this field.

The DC-bias experiments were carried out on two identical single-phase autotransformers which parameters are 250MVA/500kV. The aim of the experiments was to test the ability of transformers for withstanding DC bias. The variation laws of the electrical quantity, the temperature rise, vibration with the increasing DC current under no-load are obtained. The effective value of the exciting current under DC bias is much higher than the normal no-load exciting current. The reference for the DC bias experiments of large transformers can be provided through this method.

Based on the current situation of aviation bus detection, this paper analyzes the application limitations and shortcomings of existing detection methods. On this basis, an improved detection method in frequency-domain for two common aviation buses with different structures based on TDR technology is designed, and the detection of hard faults is used as an example to explain the detection strategy, application difficulties and solutions. Besides, the detection of soft faults for aviation bus are emphatically studied based on this method. The experimental results show that the scheme has high feasibility and good diagnostic accuracy, which provides an important reference value in engineering for reducing the difficulty and improving the efficiency of electrical fault detection for aviation bus.

Due to the environmental benefits of hybrid electric vehicles, the vehicle population would steadily grow in the next several decades. This paper analyses a large number of road data of buses. The Daily Vehicle Kilometers Travelled of new energy vehicles is 139km and that of internal combustion engine vehicles is 143km. With 50% coverage, both NEVs and ICEVs are 135km. With 80% coverage, both NEVs and ICEVs are 185km. Hybrid electric buses are charged at least once a day. The average charging frequency is 2 times a day in summary. Then construct the utility factor of bus for calculating the comprehensive energy consumption. To verify the validity, a group of experiments were carried out and a new calculation formula was conducted. The comprehensive results by using UF(B) can help users to understand actual energy consumption more clearly.

With the improvement of grid reliability requirements in social development, grid asset management must develop in the direction of digitization and informatization. Based on the latest research results of asset management and risk assessment, the article first discusses the current development status of grid asset management technology, then introduces the theoretical methods of three key technologies and their application status in asset management, i.e. state assessment, reliability and risk assessment, and Life Cycle Cost (LCC) analysis. Secondly, this paper summarizes the application of information technology in power grid asset management, and analyzes the supporting role of digital technology in asset management and its key technologies. On this basis, it puts forward suggestions for China's power grid asset management, in order to provide a useful reference for the improvement of China's power grid asset management from the technical level.

With regard to development strategy of "two networks integration", based on improvement of power supply capacity, power supply security and reliability, fault response, information collection capability as well as improvement, intelligence, integration and automation upgrade on real-time monitoring capability, "two networks integration" planning strategy and implementation plan, such as planning for distribution network, distribution itself, ubiquitous power Internet of Things, are made to greatly improve overall capacity of distribution network, which further provides reference for similar projects, such as high-quality, stable, efficient and green power supply.

With the increase of distributed generation capacity, grid-connected distributed generation (DG) has an impact on the stability of distribution network. This paper studies the influence of the access of DG on the stability of distribution network. In view of the impact of the access of DG on the distribution network, a method is proposed to improve the voltage stability of the distribution network through the configuration of energy storage equipment in the distribution network. The effectiveness of adding energy storage equipment to improve the voltage stability of distribution network is verified by the simulation results through the MATLAB / Simulink simulation model of DG.

Considering the diversification of energy and load, according to the different situation of new energy access to distribution network, three typical AC-DC hybrid distribution network power supply modes are summarized. On the basis of these three power supply modes, the indexes of AC-DC hybrid distribution network are extracted, and the comprehensive evaluation index system is established. A comprehensive evaluation model of AC / DC hybrid distribution network suitable for new energy access is proposed, which aims to maximize economic benefits. The example shows that the optimal power supply mode can be selected through the evaluation model when the proportion of new energy is different, which can lay a foundation for the future distribution network to better serve users on the premise of distribution network profitability.

It is of great significance to quickly and accurately identify power system faults. This paper introduces the idea of deep learning into power system fault diagnosis research and proposes a fault diagnosis model based on improved deep confidence network. Construct a set of 30-dimensional original features that can reflect the fault characteristics of the power system as the model input, and the fault diagnosis result is the model output. Use multi-layer Boltzmann machines to train the mapping relationship between grid faults and system features. Finally, the extreme learning machine is used to supervise the labeled samples to modify the network parameters. Different system failure scenarios were set up on the New England 10-machine 39-node system to test the diagnosis ability of the model. Simulation results show that the improved deep belief network has a strong feature extraction capability. The improved deep belief network has higher accuracy and faster speed in fault categories, fault areas, and fault locations than common artificial intelligence methods.

With the continuous and in-depth promotion of China electric power reform, the annual and monthly trading volume of electric power is increasing, the power transaction security check is mainly based on the artificial experience to add and subtract the power components that is difficult to analyse the unit group with member coupling and the power restriction relationship between transmission sections. China Southern Power Grid has developed a set of power transaction security check system using improved SCUC optimization model. The system is able to effectively locate the reason for the power transaction security check exceeding the limit and give the optimization and adjustment suggestions. The efficiency and calculation speed of the power transaction security check optimization algorithm for large scale of AC / DC hybrid power grid are successfully improved. The IEEE RTS 96 system and CSG power system are used to test the efficiency of the algorithm under different grid scales. The calculation time is proved to be reduced about 50% and generation cost is almost the same as the conventional algorithm. The construction and application of the system helps CSG to solve the surplus hydropower consumption problem.

Real-time operation risk assessment of power grid is not only an important content of power grid dispatching and operation control, but also an important reference factor in power grid accident disposal. It is difficult for the traditional evaluation methods to meet power system online operation control requirements, because of their complex process and long calculation time. To this end, a real-time power grid operation risk assessment method based on random forest intelligent algorithm is proposed in this paper, The basic concepts and implementation points of random forest algorithm is introduced. The implementation framework of real-time operation risk assessment based on random forest is proposed taking the need of power grid operation risk assessment into consideration. Compared with the traditional evaluation method, this method can calculate the power grid operation risk level directly according to some key operation indexes without complex power grid analysis, such as N-1 security check. Finally, the two-years practical application in a certain provincial power grid in China shows that the method has high evaluation accuracy and computational efficiency, which can match the traditional evaluation method and improve the calculation efficiency and accuracy of risk assessment.

This paper studied the power supply reliability and structure of the AC/DC micro-grid in the "multi-station fusion" project of smart energy stations. According to study the deep integration of the energy storage unit and the design of the data center, proposed three power supply modes of energy storage unit application to the data center server, and proposed the wiring form of the DC micro-grid. Optimized the configuration and operation mode of the diesel generators and the UPS system. On the basis of ensuring the reliability of the power supply of each system, the construction cost and operation costs were reduced, and the overall energy efficiency of the power system was improved. It provides a reference for the design and operation of "Multi-Station Integration" projects of smart energy stations.

As a large load of the smart grid, datacenters usually have a huge demand for power. Datacenter participation in demand response programs of smart grids can help the grid to adjust the load while reducing its own power costs so as to save electricity costs. In this paper, we designed a two-stage demand response participation strategy to alleviate the peak load pressure of the gird and reduce the power cost of the datacenter. The first stage determines whether to participate in demand response according to the real-time electricity prices variation of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by auction. Then the datacenter selects the tasks of the winning users to postpone processing with awards, which reduces the electricity costs of the datacenter and effectively meets the demand response requirements of the smart grid. In this way, the goals of both parties could be achieved.

Microgrid (MG) is effective to absorb the distributed renewable energy source (RES). Considering the uncertainty of RES and load, this paper proposes a fuzzy rule of energy storage system (ESS) and gets its output by anti-fuzzification in the real-time dispatch stage after receiving the actual load and RES value. Based on the envelope principle and fuzzy control, a grid-connected MG scheduling model is established. Considering the ultra-short-term forecasted data at the beginning of an hour, a receding-horizon scheduling model which includes the constraints of ladder price, ESS, RES, etc. and aims at the lowest operating cost, is established to correct the day-ahead scheduling error as the basic value of real-time scheduling at subsequent times. Taking a MG in Shaanxi Province as an example, the proposed strategy ensures the rapid and economic operation of MG.

In order to solve the voltage bifurcation phenomenon caused by the continuous change of the load on the UHV grid receiving end system, this paper proposes a two-stage control strategy based on the bifurcation trajectory sensitivity. Firstly, the mechanism of bifurcation of UHV receiving part's voltage is analyzed. Then the coordination correction model of voltage auxiliary decision and prediction control using bifurcation sensitivity is described. In order to verify the effect proposed, a typical accident of an ultra-high voltage receiving end is used for simulation. The simulation results show that the control effect can meet the stability requirements of the power grid, Two-stage Control Strategy could apply to solve the bifurcation problem.

Single-phase active power factor corrector (APFC) can eliminate harmonic current pollution to the grid harmonic current. APFC uses a double closed-loop control structure with a voltage outer loop and a current inner loop. Although this control method is very simple, the dynamic response characteristics need to be improved. After analyzing the definition of fractional PID and the design process of fractional PI controller parameter, the fractional-order operators is calculated by using the Oustaloup approximation algorithm, so that a double closed-loop variable coefficient PI controller with a voltage outer loop and a current inner loop are designed in APFC to adopt fractional PI control. The pure resistive load is taken as an example for simulation analysis and compared with the three conditions of the traditional PI controller. The results demonstrate that the APFC can obtain good dynamic and static characteristics by adopting double closed-loop fractional PI control with proper order.

The correlation between distributed generation (DG) can affect the reliability of distribution networks. To analyze the influence of this factor, first use Latin Hypercube Sampling, combine Spearman rank correlation coefficient and Cholesky decomposition to obtain the DG output power sample of the specified correlation coefficient. The bidirectional hierarchical structure reliability evaluation algorithm considering switch failure is used to evaluate the system. Considering the island operation mode of the distribution network after DG access, an improved heuristic load reduction strategy is proposed, and the reliability index of the load in the island is revised. The experimental results show that the reliability index of the system is closely related to the rated capacity of DG, the correlation between DG, and different load reduction strategies.

For transformer fault diagnosis, the three ratio method lacks of encoding, and artificial intelligence methods lack of anti-interference ability.Thus, a new method of transformer fault diagnosis based on RapidMiner and modified particle swarm optimization Extreme Learning Machine (RM-MPSO-ELM) is proposed. Firstly, RapidMiner picks out the most relevant input variables of the transformer fault. then, using the modified particle swarm algorithm to optimize the parameters for Extreme Learning Machine. Finally, using the ELM to identify the potential of transformer fault, the diagnostic performance of IEC three ratio method, support vector machine (SVM) method and different combinations of ELM algorithm are also compared. The results show that the proposed method achieves higher diagnosis precision.

With the development of the "ubiquitous power internet of things", the shortcomings of the traditional centralized power trading mechanism in power distribution side have gradually become apparent, e.g., low flexibility in power usage. And blockchain technology gains some insight to deal with these problems due to its distributed nature. To this end, the paper implements a blockchain-based multi-channel energy management model on the Hyperledger Fabric blockchain platform to carry out a transaction-driven power management method in the power distribution side. Simulation results show its operability and effectiveness.

With the development of intermittent renewable energy, power system needs more capacity resources to ensure the generation adequacy during peak load periods. Flexible resources such as electric vehicles(EVs) and demand response resources(DRRs) will play important roles in the capacity market auction. However, due to the uncertainty of outputs, they are not competitive enough in the market and face high investment risks. In order to deal with this situation, we integrate renewable energy resources(RESs), EVs and DRRs into a virtual power plant (VPP) and proposed a novel auction model for the VPP to participate in the capacity market. The objective function is to maximize the social welfare while satisfying the reliability of the system. This paper also presents the results of a case study with three scenarios to prove the validity. The proposed model provides a novel path for distributed flexible resources to participate in capacity market.

The electrical structure of the power distribution system in data center is generally a tree-like structure, with fewer branches closer to the power supply side and more branches closer to the load side. In this article, the electrical topology structure diagram is established, and the structure of power distribution network between 10kV input line and power distribution units is discussed. In the electrical structure of power system, 10kV distribution cabinets and 400V distribution cabinets are connected with electrical equipment such as electrical cable, transformer or enclosed busbar. By analysing the equivalent impedance model of equipment above, the theoretical impedance values are obtained.

This paper presents an improved failure mode and effect analysis for reliability evaluation of distribution network. By introducing the state factor of switch automation, the calculation formula of power supply reliability index of load point is modified, and the traditional consequence analysis method of fault mode is improved. Taking the test model of overhead lines as an example, the effectiveness of the improved method is verified, and the importance of distribution automation in the reliability improvement of distribution network power supply is affirmed, and the practical principle of terminal configuration is given in conjunction with the situation of distribution network in Anhui Province.

In this paper, the related concepts of Electromagnetic Interference (EMI) and Electromagnetic Compatibility (EMC) were expounded, the research progress of EMI suppression methods for power converters was summarized, and the future development trend of EMI suppression for power converters was pointed out, for example, EMI suppression method of changing circuit topology, EMI suppression method for improving control strategy, EMI suppression method of optimized driving circuit.

In this paper, a novel MTPA control scheme assisted by deep neural network is proposed based on a virtual signal injection concept. The deep neural network models the complex relationship between the electromagnetic torque and the d- and q-axis currents. The mathematical model in the conventional virtual signal injection MTPA control is substituted by the deep neural network. In this way, the MTPA control errors of conventional mathematical model based MTPA control schemes and conventional virtual signal injection based MTPA control schemes due to the neglect of the derivatives of machine parameters with respect to current angle or d-axis current can be avoided. The proposed control scheme was assessed by simulations under various operating conditions. Simulation results illustrate that the proposed MTPA control scheme could control the IPMSM operating on the MTPA points accurately and the errors caused by the neglect of the derivatives of machine parameters with respect to current angle or d-axis current were avoided.

Aimed at the puzzle of traditional PID control algorithm not satisfy level control requirement when drilling fluid performance changing, the article proposed adaptive PID level control algorithm based on differential flow. Through monitoring differential flow between slurry pump outlet and buffer pry inlet, the algorithm got optimized PID control parameters and evaluated drilling fluid properties, which could be used for liquid level control under different fluid medium characteristics. In the laboratory test of drilling fluid tank, the proposed algorithm could always keep the level deviation within 5% through self-adaptive modify PID control parameters under different drilling fluid characteristics. The results shows that the algorithm has wide adaptability and high reliability, and can be applied to the liquid level control situation where the characteristics of the fluid medium change.

Security monitoring is one of the security strategies for the supervisory control and data acquisition (SCADA) systems, and the intrusion detection system (IDS) is a main tool to do security monitoring. Main task of security monitoring is to develop the SCADA-specific IDS, which reflects the semantics of the SCADA domain. In this paper, we work on developing IDS based on deep learning models for the SCADA system. The target SCADA communication protocol of the detection model is the DNP3, which is currently the most commonly utilized communication protocol in the power substation. The attack of major concern is data injection or modification attacks, which is most critical attack in the SCADA system. We extract 12 data features from distributed network protocol 3 (DNP3) packets, and use them to train the deep neural network. We measure the accuracy and loss of the detection system trained based on different deep learning algorithms, and show the comparison of the results.

To cope with the energy crisis and the environment issues caused by the fossil fuels, the renewable energy generation developed rapidly. However, the inherent characteristics of uncertainty bring about difficulty to the operation of power system. Rolling dispatch can use the updated forecast data to adjust unit planning and it is a good method to cope with the volatility of renewable energy. But with the dispatch time resolution be finer, the computation burden of dispatch agent increases significantly and the allowable time limit of each round rolling dispatch shrunk. This paper proposed a time sections clustering method, which could adaptively reduce the number of time sections that should be considered in the optimization model. In order to avoiding the interval of two successive dispatch horizon, this paper use an extended horizon to cover a first few hours of next day. To adopt the variable time resolution, a unified unit commitment model is proposed. The numerical experiment on a real provincial system in northwest China shows the effectiveness of the proposed model.

It is a gradual evolution path and the most common practice in the initial stage of power spot market construction in China to start with bidding without price on the demand side, which lead to a question of how to solve the demand respond (DR) in day-ahead power spot market. To this end, a day-ahead power market clearing model considering incentive-based demand response (IBDR) is proposed. The IBDR is mainly aimed at large users with certain load elasticity, which can adjust their production schedules and change their electricity load curve to obtain certain cost incentives. This clearing method can optimize the spot market clearing result to maximize the social benefit by coordinating the electricity purchase cost and DR incentive cost. In this process, large users can obtain certain economic compensation by providing DR and help the dispatching mechanism to solve the operation problems such as power grid congestion, which could improve the operation efficiency of thermal power units. Finally, a case study based on the IEEE-30 buses system is used to verify the effectiveness of the proposed method.

With the large-scale integration of renewable energy sources into the power system, a new source of uncertainty is added to the operation planning problem. In this paper, the rank correlation coefficient is introduced to characterize the dependency among random variables in power flow equations, and Nataf transformation is used to map the probabilistic power flow (PPF) problem to the independent standard normal space. Dimension reduction model is introduced to approximation the function relationship between PPF inputs and outputs. Gauss-Hermite quadrature is used to obtain the statistical moments of the univariate function, whereby the statistical moments of outputs of power flow equations are obtained. Testing on an IEEE-118 system, the dimension reduction method is compared with Hong's point estimate method, it is found the dimension reduction method can improve the accuracy without extra computational burden.

For the Microencapsulated Phase Change Materials slurry(MPCMs)having a large latent heat during the phase change period and having a higher heat convection coefficient than the single-phase fluid, it has become a new thermal fluid of widespread investigation. In this paper, the model of equivalent specific heat is presented to numerical simulate the laminar convective heat transfer performances of MPCMs in a tube under the condition of constant heat flux. The heat transfer of MPCMs in a tube is investigated under various concentrations of MPCMs and heat flux and the correlation of the convective heat transfer with MPCMs in a tube is obtained.

The integrated energy system (IES) promotes the integration of renewable energy and improves energy efficiency, but it also brings uncertainty to the system. The uncertainty of the output of renewable energy may lead to imbalance of supply and demand, which brings the risk of cost increase to the economic dispatch. This paper mainly studies IES with energy hub as the research object, and studies the IES modeling and the optimal scheduling problem considering Conditional Value-at-Risk (CVaR). Firstly, the mathematical model of energy hub is established. Next, the network model of power system and natural gas system is established and the energy hub is reasonably connected to them. Then, for analyzing the uncertainty of wind power output, this paper develops scenarios using Latin hypercube sampling method and uses K-medoids clustering algorithm to reduce the scenarios. After defining the risk cost under uncertainty, CVaR is introduced into the objective function of IES optimal dispatch to describe the risk which operating personnel faces. Based on CVaR, the IES scheduling model is established and linearized into a mixed integer linear programming problem to be solved. Finally, numerical case studies verify that the IES scheduling model can reduce the risks with least costs and determine whether the system needs to avoid risks.

Coal-fired power plants are usually equipped with selective catalytic reduction (SCR) systems to reduce nitrogen oxides (NOx) emissions. In this paper, we considered both the denitrification cost of the SCR system and the coal burning cost of the boiler to develop an operation cost model. Furthermore, the genetic algorithm was proposed to minimize the total operation cost by optimizing the feature parameters, including the primary air pressure, secondary air volume, overheated air volume, and NH₃ injection volume. Simulation experiments were performed based on the operating data of a 1000 MW coal-fired power plant, with results showing that the total operating cost of the boiler and SCR was lowered.