Abstract:High rate charging will cause lithium precipitation at the negative electrode of the energy storage battery,which may induce thermal runaway of the battery and lead to safety accidents. The lithium precipitation side reaction is directly related to the negative electrode potential of the battery,and the negative electrode potential can be accurately predicted by the model and transmitted to the energy storage battery management system to adjust the charging conditions,which can effectively suppress the negative electrode lithium precipitation. Therefore,an on-line control technology is proposed for safe charging of energy storage batteries based on the simplified pseudo two-dimensional (SP2D) model. Firstly,some partial differential equations in the pseudo two-dimensional (P2D) model are reduced and simplified to establish the SP2D model. Different methods are also used to obtain the corresponding model parameters. Secondly,the model is validated using experimental data of terminal voltage and negative potential,and the validation results show that the model has high accuracy under different multiplier constant current conditions. The results show that the battery reaches the cutoff voltage of 4.3 V after 1 895 s charging,and the negative potential is in the range of lithium-free safety potential during the charging process. Finally,the simulated charging strategy is verified by cycling and disassembling,and the results show that the proposed charging method can realize the safe charging of the battery without lithium precipitation.

Abstract:With the increasing penetration rate of renewable energy,the inertia of the power grid gradually decreases,and the transient support capacity is seriously insufficient. It brings a series of security problems to the power grid. A distributed grid-connected topology cascaded by energy storage and open-winding permanent magnet synchronous generator (OW-PMSG) is proposed in this paper. By the topology,the renewable energy can be consumed. At the same time,the diesel engines can provide power backup and inertia support. By modelling the OW-PMSG and analyzing the power flow vector,a power transfer strategy based on the source-grid phase closed-loop control is designed to achieve stable synchronous operation of the generator. Based on this strategy,the energy storage converter can have synchronous generator characteristics,which achieves further improvement of grid inertia and transient support capability. The simulation results verify the feasibility of the proposed control strategy.

Abstract:With the high penetration of renewable energy in modern power system,grid-side energy storage can support power system operation as a flexible resource. Evaluating the value of energy storage is a necessary preparation for its capacity sizing. Three value indexes that reflect the value on renewable energy utilization,power system flexibility,and dispatch economy are defined at first. Then,a value evaluation method based on multi-parametric mixed integer linear programming theory is proposed and applied to above indexes. As a result,each index is represented as an analytical piecewise linear function of the power and energy capacity of storage. Finally,the proposed models and method are verified on the Northeast Power Grid data. The simulation results show that proposed method can analytically describe how the value of energy storage is affected by its capacity,along with visualization. The analytical expression contains profound sensitivity information,which reveals the bottleneck of energy storage value and provides insightful reference for energy storage capacity sizing in different application scenarios.

Abstract:Flow battery is an ideal choice for long-term and large-scale energy storage due to its advantages of numerous charge-discharge cycles,high capacity and long lifespan. However,the flow battery's complex structure poses high demands on battery control system,and traditional development methods are challenging to meet the flow battery system's diverse control requirements. To address this problem,a method for developing a flow battery control system based on event-driven technology is proposed,which aims to improve control precision and real-time performance. The proposed approach firstly presents a main-auxiliary stack synergistic architecture to address issues such as high stability requirements and large internal losses of flow batteries,and the architecture is modeled and analyzed. Then,the control system is designed by modularization based on event-driven technology,including the modules of flexible charging-discharging control,black start-up control strategy with auxiliary stacks,and state of charge (SOC) estimation based on Kalman filtering. Finally,a semi-physical simulation platform is built to validate the proposed architecture and strategies,which demonstrates that the proposed architecture and strategies can improve the energy conversion efficiency and stability of the system.

Abstract:The increase of new energy permeability increases the complexity of power system frequency control. Frequency regulation assisted by energy storage can alleviate this problem to some extent. However,due to the security and economic constraints of energy storage operation,frequency regulation measures need to be more targeted. In this paper,a primary frequency regulation method of power grid assisted by energy storage based on frequency response characteristics is proposed. Firstly,based on the model of frequency regulation assisted by energy storage,the frequency modulation integrated control method of auxiliary energy storage with inertia and droop is selected. Through the correlation analysis of rate of change of frequency and frequency deviation to frequency modulation requirements,frequency regulation demand zoning rules based on frequency response characteristics are designed. Then,according to the partition judgment corresponding to different frequency modulation requirements,the energy storage active power output mode is dynamically adjusted to respond to the uncertainty of frequency modulation requirements. On this basis,aiming at the contradictory relationship between frequency modulation demand and energy storage output demand,energy storage output strength and its cycle life,the multi-objective optimization problem is designed and solved to balance. Finally,the simulation results verify that the proposed method can effectively reduce the depth of charge and discharge of energy storage on the basis of ensuring the frequency modulation effect of power grid.

Abstract:Energy management and optimal scheduling of microgrid play importent roles in the construction of new power system. It is of great significance to study how to improve the consumption level of renewable energy,and reduce the uncertainty risk of source and load,and optimize the system operation cost. In view of this paper,a day-ahead optimal scheduling model of independent DC microgrid with generalized energy storage based on information gap decision theory (IGDT) is proposed. Firstly,a hybrid energy storage system with super-capacitor is constructed to reduce the operating cost of batteries. The flexible load with the characteristics of 'virtual energy storage' is combined with the hybrid energy storage to form generalized energy storage,giving full play to the characteristics of flexible resources in the microgrid system. Secondly,the uncertainty of the system is considered,and the IGDT model is introduced to establish the robust model under the risk aversion strategy and the opportunity model under the risk speculation strategy based on the deterministic model,so as to pursue the maximization of risk reduction and returns from two decision-making perspectives. Finally,through the simulation analysis of an example,the influence of uncertain factors on system scheduling decision is quantified on the basis of reducing the operating cost of microgrid,and the validity and reference of the model are verified.

Abstract:Considering the flexibility of shared energy storage and the different electricity demand of multiple types of users,a bi-level optimization method is proposed to achieve the optimal overall economic benefits of users and improve the photovoltaic consumption capacity for industrial users. Aiming at the lowest monthly power consumption cost of users,the upper layer model is established considering the system energy balance,energy storage charging and discharging constraints and energy storage state of charge constraints,so as to reduce the monthly maximum electricity demand and decrease demand charge. Taking the optimized monthly demand from the upper layer model,the day-ahead model is established,and the charging and discharging power of the shared energy storage,as well as the interactive power between users and the power grid are output to optimize comprehensive benefits of multiple types of industrial users and improve the photovoltaic absorption ability. Strategies for shared energy storage charging and discharging power,as well as user interaction power with the grid,are proposed to optimize the comprehensive benefits for various types of industrial users,ensuring daily economic efficiency under the premise of monthly overall economic optimality. Finally,the effectiveness of the proposed method is verified with the case study.

Abstract:In the procedure of carbon peaking and carbon neutrality energy revolution,large-scale renewable energy is aggrandizing frequency regulation capacity of power grid. How to make the best use of new rapid resources,represented by battery energy storage systems (BESS),in the secondary frequency regulation is the key to solve the problem. Firstly,a three-layer frame is designed,embodying area,cluster and plant,which can satisfy the needs for access monitoring and classification decision of various regulation resources in automatic generation control (AGC) system. Secondly,a coordination control strategy including BESS is proposed,and so is the strategy of regulation requirements assignment based on state of charge (SOC) effecting factor in BESS group. Finally,simulations are conducted for continuous disturbance cases from an actual power system,as well as simulations for tripping fault disturbances. The results show that the proposed strategy can improve the SOC consistency between the respective BESS,promoting the availability of BESS,and improve the performance of network frequency response characteristics either.

Abstract:High-voltage direct current system embedded in large-scale AC power systems will further increase the impact of commutation failure,DC blocking and other faults on system security and stability. The interaction between AC system and DC system will make the system dynamics more complex. In this paper, an intelligent analysis method based on electromechanic-electromagnetic hybrid simulation and machine learning is proposed to analyze the complex dynamic response characteristics of power system after large disturbances under different operation modes. A two-stage clustering model based on principal components analysis (PCA) dimensionality reduction,density-based spatial clustering of applications with noise (DBSCAN),K-means and other algorithms is built in this method,which can automatically cluster a large number of hybrid simulation dynamic curves in high-dimensional space, give corresponding identification and severity,and extract typical dynamic patterns of AC-DC systems under different faults. The leading security and stability problems in each mode are labeled and identified. The effectiveness of the proposed method has been verified in the planning model of East China Power Grid in 2025. The results show that the proposed method can effectively extract the system dynamic mode under different faults and support the dynamic mechanism analysis of AC/DC system under subsequent complex faults.

Abstract:With the rapid development of wind industry in China,it plays an important role for the promotion of energy transition to exploit wind energy in a large-scale format. However,due to the complicated environment and high construction and operation cost,it faces a series of technical difficulties and challenges to develop the wind farms in the format of a cluster. In view of this,the prediction,planning,and control technologies related to the construction and operation of wind farm cluster are summarized in this paper. Specifically,the key technologies and research route of the wind farm cluster are summarized in this paper. According to the characteristics of the wind farm cluster,the quantitative characterization and prediction of wind resources,the coordinate optimal planning of multiple wind farms,and the control and operation of multiple wind farms are discussed in detail. The current research status and achievements in each technology field are analyzed in this paper. Lastly,the development trend of the construction and operation of wind farm cluster is illustrated and the technical difficulties to be addressed are pointed out in this paper. The summary of these technical research achievements can provide reference for the large-scale and clustered development of wind power in China.

Abstract:The high voltage direct current (HVDC) converter has a certain dynamic reactive power regulation ability. Making full use of the reactive power regulation ability of the converter station can significantly improve the stability of the HVDC. In this paper,the adjustable capability of reactive power of HVDC during steady-state operation is studied,and the coupling characteristics of active power and reactive power are analyzed. Taking conference International des grands reseaux electriques (CIGRE) HVDC standard test model and Guiguan Ⅱ direct current transmission engineering model as examples,the operating range of direct current under steady-state condition is analyzed,and then the adjustable capability of reactive power on both sides of rectifier and inverter is obtained and applied in reactive power control. It is found that CIGRE HVDC standard test model has similar regulating capacity of capacitive reactive power and inductive reactive power,while regulating capacity of inductive reactive power is higher than that of capacitive reactive power in Guiguang Ⅱ direct current transmission engineering model. The application value of reactive power adjustable capability is verified in electromagnetic transient simulation program PSCAD/EMTDC.

Abstract:For the traditional low voltage ride through control under asymmetrical faults,due to the limited control degrees of freedom,the grid-connected inverter control suffers from the problem of not being able to simultaneously realize the output current negative-sequence component suppression and the DC side voltage double-frequency fluctuation suppression. In this regard,a multiple-objective decoupling control strategy is proposed for low voltage ride through of two-stage grid-connected photovoltaic system under asymmetrical faults. The strategy sets the control objective of the inverter as the negative-sequence component suppression of the output current and gives a reference value calculation method for the inner current loop considering the inverter output current limit and reactive power output demand. The super-capacitor is connected to both ends of the DC bus capacitor through the bidirectional Buck-Boost converter to maintain its voltage stability,and the DC side voltage double-frequency fluctuation is transferred to the super-capacitor side. The simulation results show that under the proposed control strategy,the unbalance between three phases of the inverter is reduced and the output current distortion is improved. The double frequency fluctuation of the DC side voltage is reduced significantly compared with the traditional control method.

Abstract:Aiming at the problems that multiplex influencing factors and strong uncertainty in distribution network load caused by the capacity accumulation of distributed generation and new loads,a load prediction method using memory neural network and curve shape correction is proposed. In load peak prediction,the maximum information coefficient is applied to calculate the nonlinear correlation between load peak and influencing factors,so as to select the input features. Considering the long-term and short-term autocorrelation in load peak sequence and the different correlation between input features and load peak, the load peak prediction model is established with the Attention mechanism and bidirectional long-short term memory (BiLSTM) neural network. In load per-unit curve prediction,a prediction model is established by combining similar day and adjacent day through the reciprocal error method. In view of the non-stationary characteristics of prediction deviation,the complete ensemble empirical mode decomposition with adaptive noise and BiLSTM network are used to establish an error prediction model to correct the curve shape. The validity of the proposed model is verified by an example of regional power grid load of a city in northern China.

Abstract:Renewable energy has weak feedforward and controlled characteristics,so the traditional industrial frequency protection does not work when renewable energy is sent out through AC transmission line. On the contrary,the traveling wave protection methods can judge the fault before the control system responds,which are one of the effective ways to solve the problem encountered in renewable power system. However,the existing traveling wave protection schemes are limited by the sampling rate,which makes it difficult to accurately identify the internal near-end fault and the internal remote-end fault. To solve this problem,the grid diagram of traveling wave in case of internal and external faults is analyzed at first. Then,the time difference between zero-mode and line-mode is used to judge the fault location. For the near-end fault and remote-end fault which are difficult to be accurately identified based on the time difference,the first reverse polarity traveling wave is used to identify the near-end fault and the polarity of the first two line-mode traveling waves is used to identify the remote-end fault. Simulation results show that the proposed scheme can quickly identify internal and external faults. At the same time,the proposed scheme has good ability to withstand transient resistance.

Abstract:As a new power electronic equipment,energy routers (ERs) can realize the flexible distribution of electric energy in power systems. It is of great significance to analyze the influence of energy routers on the system and study the power flow calculation methods of AC-DC hybrid distribution networks with ERs as the distribution hub. In this paper,the steady state power flow model of ERs is established based on the improved alternating iteration method,and the droop control strategy is adopted for the DC port of the ERs. Combined with the traditional decoupling method,an AC-DC decoupling iterative power flow calculation method suitable for the power distribution network containing ERs is proposed. An IEEE 14-node and IEEE 69-node system with multiple ERs are used as examples to verify the correctness and convergence of the proposed method. In order to analyze the influence of ERs on the system operation,the simulation calculation of IEEE 69-node test system in different scenarios proves that ERs can support the node voltage in the system and reduce the system operation loss.

Abstract:Comparing to traditional onboard chargers,integrated onboard charger system (IOCS) takes obvious merits in terms of cost and power density. In this paper,an IOCS based on a six-phase permanent magnet motor drive is designed,and model predictive current control (MPCC) methods are studied for the IOCS under the grid-connection modes. At first,the topology of the IOCS is analyzed and the mathematical model is established. Following this,the implementation of traditional MPCC is also introduced. Then,a MPCC based on duty cycle optimization (DCO-MPCC) is proposed to overcome the disadvantages of the traditional MPCC including high computation burden and bad steady-state performance. On the one hand,the computation burden is alleviated by reducing the number of the alternative voltage vectors. On the other hand,a duty cycle optimization technique is proposed to enhance the steady-state performance. Finally,the effectiveness and superiority of the proposed control strategy are verified using experiments. The experimental results indicate that the proposed control strategy can significantly enhance the steady-state performance of the system and reduce the computation burden. The total harmonic distortion (THD) of grid current is reduced by 6.18% and 5.92% under charging and vehicle to grid (V2G) operations,respectively. Meanwhile,the execution time of the proposed strategy is decreased by 17.54 μs.

Abstract:Power system planning,load forecasting,and energy utilization analysis are significantly impacted by power load anomalies,necessitating prompt detection and identification. Firstly,the abnormal classification,causes,and characteristics of power load data are analyzed. Secondly,it enhances the traditional Transformer encoder structure by replacing the mask multi-header attention layer with the multi-header attention layer and eliminating the feedforward network. These improvements aim to enhance the model's global attention to the load sequence. To capture trend characteristics more effectively and expedite convergence,an improved generative adversarial networks (GAN)-Transformer model is proposed based on the generator and discriminator game structure of traditional GAN. Additionally,a multi-stage mapping and training approach along with an integrated focus score scoring mechanism are introduced. These techniques facilitate phased load sequence reconstruction,enabling the model to better extract anomalous features of the load data. Finally,through an analysis of an arithmetic example,the results demonstrate that the GAN-Transformer model outperforms in terms of load data anomaly detection accuracy,recall,F₁ value,and training time. These results validate the effectiveness and superiority of the proposed method. The findings of this research provide valuable insights for advancements in power load data anomaly classification and data repair based on deep learning.

Abstract:In this paper,a non-intrusive load identification algorithm for residents based on prior knowledge and statistical learning model is proposed to solve the problem of insufficient electric heating subdivision capability in traditional identification technology. In this paper,the electric heating subdivision research is carried out for the auxiliary heating equipment of washing machine,electric kettle,electric rice cooker,electric water heater. The subdivision of auxiliary heating equipment is realized through the equipment operation association algorithm,and the model training of non-auxiliary heating equipment classification is realized based on the limited feedback information of users and expert annotation. The experimental results show that the technical framework proposed in this paper realizes the subdivision of electric heating equipment on the basis of the event detection load identification algorithm and F1 socre above 0.9 is achieved in the decomposition of operation state.

Abstract:The ablation fault of buffer layers in high-voltage cables is a frequent type of cable fault in recent years. However,current detection methods for ablation defects cannot meet the detection requirements of existing cables. In this paper,the ultrasonic detection method for the buffer layer ablation defects in high-voltage cables based on the surface roughness of aluminum sheath is studied for the first time. Firstly,simulated buffer layer ablation experiments under humid conditions are carried out,and the laser confocal microscopy and electrochemical impedance spectroscopy analysis are carried out on the ablated aluminum specimens. It is found that with the increase of ablation time,the surface roughness of aluminum specimens gradually increases,and the surface corrosion degree of aluminum specimens gradually deepens,corresponding to the aggravated ablation defects of buffer layers,which indicates that the surface roughness of aluminum specimens could be correlated with the ablation degree of buffer layers under the moisture conditions. Secondly,ultrasonic testing experiments are carried out on the ablated aluminum specimens,and the roughness of the corroded surfaces of aluminum specimens can be calculated from the amplitude ratio of adjacent ultrasonic echo signals. The calculated results show the same variation trend as the measured roughness in the experiment. The results in this study indicate that the ultrasonic tests could be used to detect the severity of buffer layer ablation defects,providing the research basis for the ultrasonic detection method of ablation defects in the buffer layer of high-voltage cables.

Abstract:Ultra high frequency (UHF) partial discharge (PD) detection is a common method for transformer oil-paper insulation defect location. However,the accuracy of PD positioning in practical application is easily affected by noise and sensor layout. In order to ensure the effectiveness of PD positioning detection of transformer oil-paper insulation defect,an UHF PD positioning detection platform for oil-paper insulation defects is established. Based on the conventional K-means method,a modified clustering demarcation PD anti-interference positioning method is proposed,which effectively reduces the positioning error. Aiming at the problem of sample clustering demarcation aliasing,when the optimal correction coefficient L is 1.1,the UHF PD positioning error can be reduced to less than 0.1 m,which verifies the effectiveness of the proposed method. Finally,the variation law of positioning error of different sensor layout is analyzed,and the optimal layout scheme of UHF sensor for transformer oil-paper insulation defect detection is proposed,which can provide reference for the layout and positioning of transformer on-line monitoring sensor.

Abstract:Dissolved gas analysis is important for the early warning and diagnosis of transformer faults. Aiming at the problems of numerous types of features for dissolved gas in oil and the insufficient analysis of fault associated features,a new fault diagnosis method for oil-immersed transformers based on feature selection of dissolved gas in oil is proposed. Firstly,the derivation of original features for dissolved gases is completed. The optimal combination of features is selected by calculating the importance of features for fault diagnosis based on random forest (RF). Then,the tree-structured parzen estimator (TPE) is used to realize the parameter optimization of the RF model,and the TPE-RF diagnostic model is obtained. Combined with the various evaluated indicators,the proposed method is proved to be able to diagnosis the transformer faults accurately. Finally,the TreeSHAP model is introduced to analyze the importance of the features corresponding to each fault, and the specialized features for each fault are selected. According to the case of transformer in operation,the applicability of the method in the power system is discussed,and the effectiveness of the method is verified.

Abstract:DC contactor is one of the most widely configured switching appliances in new energy systems and electric vehicles. The dynamic characteristic of DC contactor is one of the important indexes to measure the performance of DC contactor. A dynamic model of the electromagnetic mechanism of DC contactor with parallel double coil type is established in this paper. Dynamical model of the electromagnetic mechanism of DC contactor is simulated. The closing process simulation analyzed of the electromagnetic mechanism of DC contactor by using the co-simulation research method based on Adams and Maxwell. The research results show that the simulation closing time of the closing action the mechanism is consistent with the test closing time. The correctness of the simulation method is verified. At the same time,the influence of spring parameters,coil parameters and other factors on the closing motion characteristics of the mechanism is studied. The change of spring pre-pressure has little influence on the closing time within a certain range. The increase of coil ampere turns will lead to the decrease of closing time, the increase of coil internal resistance will lead to the increase of closing time and the more severe bouncing of moving contact. The simulation method and results can provide some reference for the improvement design and optimization of DC contactor electromagnetic mechanism.

Abstract:In order to prevent the faults caused by the overheating phenomena of gas insulated switchgear (GIS) contacts,it is necessary to monitor and predict the temperature of GIS contacts. In view of the problems that the temperature of contacts is not easy to be directly measured and the temperature is easily affected by the operation conditions and external factors,a prediction method of GIS contact temperature based on multiple parameters is proposed in this work. The temperature distribution law of GIS under different influencing factors is investigated by employing a three-dimensional simulation model. The influencing factors are contact resistance,load current,ambient temperature,wind speed,SF₆ pressure and solar radiation intensity. The reliability of the model is validated by use of heat circuit model. The results indicate that the key factors to predict the contact temperature are the shell temperature rise,load current,wind speed,SF₆ pressure and solar radiation intensity. At the same time,the influence of ambient temperature can be ignored. Further,the back propagation (BP) neural network is adopted to predict the temperature rise of the contacts with the above factors. The predicted temperature rise is compared with the calculated one of the model,and the error is in the range of -0.70~0.68 ℃. The method comprehensively takes into account the influence of various factors on the GIS temperature field,and it helps to give a reference for the temperature prediction of GIS contacts based on external sensors.

Abstract:The disconnector fault in gas insulated switchgear (GIS) is commonly caused by the combined effects of mechanical, thermal,and electrical factors. Throughout the progression from defect development to a severe fault,various signals, including abnormal vibration,temperature changes,and partial discharge,are generated. The simultaneous measurement and analysis of multi-parameter information pertaining to the GIS disconnector are crucial for accurately determining its operating condition. This study focuses on simulating typical defects in a 220 kV GIS disconnector to investigate the evolution patterns of vibration signals,temperature variations,and partial discharge signals under different contact states. The aim is to establish the correlation between these signals and the condition of the GIS disconnector. Additionally,the study proposes a comprehensive evaluation method for GIS disconnector by incorporating the health index theory and an improved analytic hierarchy process. The proposed method is subsequently applied to practical GIS,demonstrating consistency between the identified fault and the analysis results obtained through the multi-parameter comprehensive evaluation approach. Overall, this research introduces a highly feasible new method for the detection of GIS disconnector conditions.

Abstract:In the novel power system of urban grid,the multiple resources increase and the data collection becomes more difficult,which lead to a higher random missing data rate. It is difficult to meet the demand for refined analysis and decision making. For the frequent missing data problem in the distribution network,a new missing data filling method for power systems based on fluctuation cross-correlation analysis (FCCA) and generative adversarial network (GAN) is proposed in this paper. Firstly,a multi-dimensional feature extraction method for strongly correlated grid data is proposed by fusing FCCA. Secondly,based on kernel principal component analysis (KPCA),the multi-dimensional feature dataset is dimensionally reduced. Finally,an improved GAN structure is designed,which integrates multi-dimensional features of power grid equipment data to reconstruct low dimensional vectors. The missing data is accurately filled in,and the integrity and availability of the new power system measurement data is improved. The algorithm is validated using real grid data,and the proposed method is also tested in a city grid. The results show that the proposed method has higher filling accuracy than the traditional data filling methods. Therefore,it is conformed that in the case of continuous and significant data environment,integrating strong correlation features for data filling has significant advantages in improving the integrity and availability of measurement data.

Abstract:The inter-turn short circuit fault of wingding is a typical serious fault of the new synchronous condenser,which has a serious impact on the long-term operation of the condenser and the stability of the power grid. In order to ensure the reliable and stable operation of the new synchronous condenser,a method of online diagnosis and location of rotor inter-turn short circuit fault is proposed in this paper. Based on the distortion of the air-gap magnetic field magnetomotive force caused by fault it is deduced that the phenomenon of even harmonic circulation will occur in the same phase and double branches of the stator. Through Fourier decomposition of the branch circulation,the obvious characteristic quantity of harmonics is extracted and analyzed. It is concluded that the characteristic quantity is only related to the spatial position of the rotor where the fault is located,and has nothing to do with the number of fault turns. Therefore,this characteristic quantity can help realize fault diagnosis and location. Based on the TTS-300-2 type synchronous condenser which has been put into use on a large scale,a simulation model is established with the actual condenser in the finite element software while an experiment platform is developed with a condenser simulator for experiments. The simulation and experimental results validate the effectiveness of locating rotor inter turn short circuit faults through branch circulating current harmonics.

Abstract:Photovoltaic power generation and photothermal power generation are two forms of solar power generation. The development cost of photovoltaic is relatively lower than photothermal,but a certain proportion of energy storage and distributed synchronous condensers are needed to ensure the safe and stable operation of the system. As a result the cost of photovoltaic power generation rises. Photothermal comes with heat storage and output friendliness,so currently there is still a great dispute about the technical route of photovoltaic and photothermal. This paper takes Haixi area of Qinghai province as an example. Firstly,based on the characteristics of solar irradiation resources and output in the same time and space,the power guarantee capabilities of photovoltaic+energy storage and photothermal is compared. Then this paper compares photovoltaic+energy storage+condenser with photothermal from the perspective of power grid security and stability support ability. Finally,this paper compares the economics of solar thermal and photovoltaic based on the forecast of their cost declining trend. Photothermal has both thermal storage and conventional generator functions, providing short circuit capacity,reactive power compensation and moment of inertia support for the system. The results show the economy of photothermal is basically equivalent to,or even better than photovoltaic+energy storage+condenser after the cost reduction in the case of technical equivalence.