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The design of control and protection system of the line-commutated converter (LCC) in a meticulous way should consider both dynamics of the LCC and large-scale ac grids. Consequently, a highly accurate and efficient simulation method is desirable. In order to capture the high-frequency dynamics between ac/dc grids, the multirate and mixed solver based cosimulation by combining transient stability (TS), shifted-frequency phasor (SFP), and electromagnetic transient (EMT) models is proposed. The idea of the proposed multirate and mixed solver based cosimulations is to naturally decouple the whole system into the respective TS, EMT, and SFP subsystems. The accuracy of the proposed multirate cosimulation is guaranteed by the proposed interface models, i.e., the TS–SFP interface model (TS–SFP–IM) and the SFP–EMT interface model (SFP–EMT–IM). The efficiency is guaranteed by the natural partitioning strategy, decoupled manner based communication structure and the overall realization of time sequence. The performance of the proposed multirate cosimulation has been validated on practical ac/dc system in China. [ABSTRACT FROM AUTHOR]

Due to the geographic features and fruitful water resources, the distributed generation of Small Hydropower Generator Stack (SHGS) are vigorously developed in southwest China. As the more concerns about security and stability of power grids with the large amount of SHGS feed into the grid, the accurate models are critical to analyze those potential impacts. With the development of online monitoring technologies in smart grid, PMU based dynamic equivalent modeling for SHGS drawn many attentions. However, field applications reveal that traditional SHGS equivalent model may not be robustness enough to adapt the different disturbances. To overcome this deficiency, method to improve the robustness of SHGS dynamic equivalent modeling is proposed. In the paper, a coherency identification based multi-machine modeling method is introduced to enhance the accuracy of equivalent model. Sensitivity and correlation analysis based key parameters selection scheme is implemented to select the critical parameters, meanwhile avoiding the multiple solutions problem. Moreover, multi-objective optimization algorithm is designed for key parameters identification of the equivalent model. Additionally, hybrid dynamic simulation has adopted into the equivalent process to improve the computational efficiency. The effectiveness of the proposed method has been validated with an actual case from China Southwest grids. [ABSTRACT FROM AUTHOR]

Due to the maturity of wide area measurement technology, phasor measurement unit (PMU)-based dynamic equivalent modeling method (PDM) has been wildly used in large-scale power system modeling. However, traditional “event-based” PDM equivalent model may face serious robustness issues, such as although derived equivalent models show the good ability on reproduction of the system behaviors under recorded events where they derived from, they may not work well for other possible disturbances. In this article, to improve the robustness of equivalent model, a novel method on dynamic equivalent modeling of power system is proposed. Based on the result of coherency identification, generators inside the equivalent system can be aggregated into different coherent subgroups and represented by multimachine equivalent model. Key parameter selection, which can shrink the scale of identified parameters, is designed to avoid multiple solutions of the equivalent model. Moreover, a multiobjective parameter identification algorithm is developed to fully consider the PMU measurements in different system events. In addition, hybrid dynamic simulation is performed so that each equivalent submodel can be identified separately. Finally, the effectiveness of the proposed method is verified in an actual power grids in China. [ABSTRACT FROM AUTHOR]

• This study concerns the risk of power system caused by magnetic storms. • Responses of power system to magnetic storms are studied. • Excitation current is proposed to be a suitable quantity for assessing the response. Geomagnetically induced currents (GICs) flowing through transformers result in a DC bias, which poses a threat to safe and stable power grid operation. To evaluate the influence of geomagnetic storms on power systems, it is necessary to study actual power system responses. In this paper, the power system response is represented separately using current and reactive power measurements acquired using phasor measurement units (PMUs). On the basis of the data obtained from a wide-area measurement system, the power system responses to geomagnetic disturbances at Yangquan Substation, Shanxi, China were obtained. By employing the plane wave method, the data acquired during two mild magnetic storms at the Shisanling Geomagnetic Observatory were used to calculate the electric field and GIC at Yangquan Substation. The response variables and calculated GIC results agreed well, and the response variables were thus applied to assess the GIC level in the power system. The results show that the fluctuations in the PMU measurements were caused by GICs in the power system, laying the foundation for a significantly more detailed geomagnetic storm risk monitor. [ABSTRACT FROM AUTHOR]

In order to accurately and effectively diagnose the transmission line faults of the power system, a genetic-algorithm support vector machine and the D-S evidence theory based fault diagnostic model is proposed. Two genetic-algorithm support vector machine based diagnosis boxes separately identify the fault types to get two kinds of preliminary diagnostic results according to the voltage and current phasors data from the phasor measurement units. Then, a deviation coefficient is derived to represent the diagnostic conflicts existing in the two kinds of preliminary diagnostic results. The mass function of the D-S evidence theory is updated by using the deviation coefficient to eliminate the diagnostic conflicts and improve the diagnosis accuracy. Finally, the proposed diagnostic model was applied in an actual power system in Hubei province in China. The simulation results and practical results indicated the feasibility and the effectiveness of the diagnostic model. [ABSTRACT FROM AUTHOR]

• Four algorithms are used for ambient signal based load model parameter identification. • Improvements are proposed to validate the optimization ability for the algorithms. • Comparisons involve the identification accuracy, calculation time, and the robustness. • Simulation and the field PMU data show the different performance of the algorithms. The time-varying characteristic of the power load can be tracked with the ambient signal based load model parameter identification method proposed recently. Meanwhile, the small fluctuations of the ambient signal bring higher requirement for optimization algorithm as well. The interior-point algorithm (IPA) and the active-set algorithm (ASA) are two typical traditional nonlinear constrained optimization algorithms, while the differential evolution algorithm (DEA) is an excellent heuristic search algorithm. Besides, the grid search algorithm (GSA) is a violent search algorithm, which is also an effective method considering the fact that the real load model parameters are unknown in practical power system. Based on the review of the basic idea of the four algorithms, the applicability of the four algorithms is comparatively assessed for ambient signal based load model parameter identification. Their performance is compared using ambient data from both simulation and field phasor measurement unit (PMU) in China Southern Power Grid considering the identification accuracy, calculation time, and the robustness to measurement error and algorithm parameters. Some general conclusions are drawn from the analysis for several cases. [ABSTRACT FROM AUTHOR]

• A generalized integer linear programming model of the PMU placement is proposed. • Topology changes are considered to guarantee the observability with multiple topologies. • The effects of existing measurements and their relevance are covered. Distribution networks usually have a meshed structure but are operated radially to improve the operating efficiency and ensure the power supply under an emergency situation. It is of great significance to guarantee the observability of the entire system under the topology changes in operation. This paper proposes an optimal placement method of phasor measurement unit (PMU) for distribution networks. A generalized binary integer linear programming (ILP) model for PMU placement is proposed. The changes in topology are considered to guarantee the observability under any possible operation mode. The existence of zero injection nodes (ZINs), the existing measurements, and their relevance are also covered in the proposed method to reduce the installed PMU number. The objective of the ILP problem is weighted by the degree of the corresponding node to obtain a scheme with higher measurement redundancy. The correctness and effectiveness of the proposed method are verified via case studies on the IEEE 33-node test feeder, PG&E 69-node test feeder, and a medium voltage distribution network in Southern China. [ABSTRACT FROM AUTHOR]

Timely, effective, and robust artificial intelligence (AI) technology is urgently needed to improve decision-making efficiency in the presence of renewable energy with high penetration by elevating the level of power grid intelligence. However, at this stage, AI technology lacks reliability and transparency, making it unable to play a full role in application areas with high-security requirements such as power systems. This paper presents a multi-hierarchical interpretable method for power system dispatch and operation based on the graph deep Q-network (GDQN) model to achieve active power corrective control. The multi-hierarchical interpretable method combined with an improved sample-balanced deep shapley additive explanation (SE-DSHAP) method and a subgraph explainer can promote a more intuitive and comprehensive explanation of decision-making for power systems with complex topology. Operators will obtain more noteworthy power grid areas through the proposed interpretable method as the basis of auxiliary decision-making to realize efficient and accurate control. Then, two cases studied in a modified 36-bus system in the IEEE 118-bus system and the 300-bus network of the China power grid validate the effectiveness of the proposed method. • A graph deep Q-network model achieve active power corrective in power systems. • The SE-DSHAP method improve the computational speed and accuracy. • A subgraph explainer bring a comprehensive understanding of the model predictions. • Two cases validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Considering "buckets short slab effect" during the overall vulnerability assessment. • Incorporating overall vulnerability assessment into the economic-oriented TEP optimization model. • Taking the overall vulnerability as one of the objectives for enhancing the power grid resilience. • The generated Pareto optimal solutions help planners make a trade-off between economy and resilience. During recent decades, blackout accidents caused by cascading failures worldwide raised concerns of power grid operators and researchers about enhancing power grid resilience to withstand disruptions. As an effective approach to identifying critical components and quantitatively measuring transmission networks' weaknesses, vulnerability assessment (VA) is instrumental for power grid resilience enhancement. This paper proposes a transmission expansion planning (TEP) model incorporating the VA to improve the resilience of the grid planning scheme. Firstly, a structural vulnerability index (SVI) and an operative vulnerability index (OVI) are introduced for the VA of buses. The affinity propagation clustering technique is adopted to reduce the dimension of the large masses of SVI/OVI data. In particular, considering that the vulnerability of a power grid largely depends on the most vulnerable components rather than the rest, the overall vulnerability is evaluated through both the mean and equilibrium value of the clustered SVI/OVI data. In order to integrate expert opinions and objective factors of the clustered SVI/OVI data, the analytic hierarchy process (AHP)-entropy method is adopted to design the weights in the overall VA for a TEP scheme. Finally, as an inverse problem of the overall VA for a TEP scheme, a multi-objective TEP optimization model is proposed, which minimizes the overall vulnerability as one of the objectives for enhancing power grid resilience. Case studies based on the IEEE 39-bus system and two practical TEP cases in central and southwestern China demonstrate the effectiveness of the proposed overall VA method and the TEP optimization model, which can help planners make a trade-off between economic investments and resilience enhancement. [ABSTRACT FROM AUTHOR]

• The data quality of field PMU measurements is analyzed, the results of which indicate the necessity of data improvement for measurement based potential applications. • A robust state estimation method is adopted to improve the data quality of field PMU measurements for ambient signal based load model parameter identification. • Two scenarios, i.e., data recovery and data correction are considered for the effectiveness verification of the state estimation method for data improvement. • Case studies with not only simulation data from power system analysis toolbox, but also the field PMU measurements from China southern power grid, demonstrate the effectiveness of the state estimation method on data improvement for ambient signal based load model parameter identification. Considering the component based modeling approach's shortage of requiring the typical load model parameters and the traditional measurement based modeling approach's inadequacy of the dependence on fault-disturbance data, the recently proposed ambient signal based load modeling approach offers an important idea to track the time-varying characteristics of power loads. However, it suffers the impacts of phasor measurement unit (PMU) measurement errors. Consequently, data improvement for ambient signal based load modeling is both important and necessary. In this paper, state estimation is adopted to make the ambient signal based method usable with field PMU measurements. To be specific, a weighted least square method is applied for state estimation to improve the data quality. On this basis, the improved data are used for load model parameter identification to evaluate the performance of the state estimation method from the perspective of data application. Case studies with both simulation data from power system analysis toolbox and field PMU measurements from China southern power grid have verified the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Recently, energy efficiency measurement has been emphasized in China for energy conservation purpose, while there are appealing requirement for plug-and-play measuring manner without long outage time. Different with previous approaches, we present a sparse fault tolerant (SFT) method to calculate electricity energy efficiency under IEC PC-118 cloud architecture to accommodate the low speed data acquisition network. It is implemented with only one modification of the incoming line to monitoring the bus voltage. An attenuation distributed approximating approach is developed for fundamental, harmonic and interharmonic frequency and phasor estimation during energy measurement. The performance is verified with different SNR, and the results show that the proposed approach is highly resilient to noise can works well under sparse sampling environments. To guarantee the security of the power system, the trivial signal disturbance is involved as additional noise to verify the performance of SFT, and the performance is also compared with several typical algorithms, such as DFT, WIDFT, S-LMS, IDFT. The experimental results show that SFT can be used under noisy environment for SFT approach and the accuracy can be improved by the selection criteria of residues rather than improve the sampling rate. It can be applied to any form of signal and can be used online without blackout or power line interruption. [ABSTRACT FROM AUTHOR]

Synchrophasors, widely used in the monitoring and analysis of power systems, evolved from the phasor method presented by Charles Proteus Steinmetz in 1893. The phasor method is a mathematical method for solving linear sinusoidal steady-state circuits and time-varying electromagnetic fields. This paper traces the history and diffusion of the phasor method in the discipline of electrical engineering in China. In 1914, Sidney Roby Sheldon, an American teacher at the Government Institute of Technology of the Communications Ministry, Shanghai, China, introduced the phasor method to his students in his Alternating Currents course. The textbook used was Elements of Electrical Engineering (Volume II) by William Suddards Franklin and William Esty. In 1920, The Electrical Magazine published a paper “Complex number and its application” by Sijiu Shi (editor of the electrical engineering section of this periodical). This is the earliest example found of Chinese literature introducing the phasor method. In the 1930s, Principles of Alternating Currents, an American electrical engineering textbook that includes the phasor method, was translated by Chinese scholars; this Chinese version promoted the use of the phasor method in China's electrical engineering field. Alternating Current Circuits, authored by the Chinese university professor Pen Tung Sah and published in 1948, may be the earliest example of the phasor method being included in a Chinese electrical engineering textbook; it played an important role in popularizing the phasor method in China. [ABSTRACT FROM AUTHOR]

The dynamic behavior of phasor measurement units (PMUs) is not only the key point to dynamic phasor algorithm improvement but also the basis for the successful implementation of PMUs in power system dynamic security monitoring and control. In this paper, the dynamic performance of PMUs is evaluated, and their dynamic behaviors are analyzed. PMUs from the four largest Chinese manufacturers are tested through the established PMU test system, and their dynamic performance is comprehensively evaluated. The dynamic behaviors of the PMUs are also analyzed. Analysis shows that the spectral leakage and the averaging effect of the discrete Fourier transform (DFT) under dynamic conditions are the key reasons that lead to their dynamic behaviors. The theoretical results are consistent with the experimental data and the simulation. Finally, to eliminate the spectral leakage, the DFT with a digital low-pass filter is adopted to obtain more precise phasor measurements. [ABSTRACT FROM PUBLISHER]

• A lossless phase angle conversion method is proposed to enhance the compression performance of phase angle data of PMU. • An improved SVD-based compression method for PMU is proposed. • An error control strategy is designed for the dynamic CR adjustment. The integration of phasor measurement units (PMUs) greatly improves the operation monitoring level of distribution networks. However, high sampling rates in PMUs generate huge volumes of measurement data, which creates heavy transmission and storage burdens in information and communication systems. In this paper, an improved singular value decomposition (SVD)-based data compression method for PMU measurements in distribution networks is proposed. First, a lossless phase angle conversion method is proposed, which converts the discontinuous phase angle data of PMU into continuous data sequence to enhance the compression performance. Then, a PMU data compression method is proposed based on SVD, and the compression capability is further enhanced by a lossless compression that utilizes the orthogonal property of the two sub-matrices generated by SVD. Moreover, an error control strategy is designed to dynamically optimizes the scale of transmitted data according to the accuracy requirement of different applications in distribution networks. Finally, case studies are performed using real PMU measurement data from a pilot project in China to validate the compression performance and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Even with modern smart metering systems, erroneous measurements of the real and reactive power in the power system are unavoidable. Multiple erroneous parameters and measurements may occur simultaneously in the state estimation of a bulk power system. This paper proposes a gross error reduction index (GERI)-based method as an additional module for existing state estimators in order to identify multiple erroneous parameters and measurements simultaneously. The measurements are acquired from a supervisory control and data acquisition system and mainly include voltage amplitudes, branch current amplitudes, active power flow, and reactive power flow. This method uses a structure consisting of nested two loops. First, gross errors and the GERI indexes are calculated in the inner loop. Second, the GERI indexes are compared and the maximum GERI in each inner loop is associated with the most suspicious parameter or measurement. Third, when the maximum GERI is less than a given threshold in the outer loop, its corresponding erroneous parameter or measurement is identified. Multiple measurement scans are also adopted in order to increase the redundancy of measurements and the observability of parameters. It should be noted that the proposed algorithm can be directly integrated into the Weighted Least Square estimator. Furthermore, using a faster simplified calculation technique with Givens rotations reduces the required computer memory and increases the computation speed. This method has been demonstrated in the IEEE 14-bus test system and several matpower cases. Due to its outstanding practical performance, it is now used at six provincial power control centers in the Eastern Grid of China. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable energy, such as hydro, wind and solar, curtailment is a serious issue in some regions, such as China. The key issue is lacking of flexibility, primarily due to the limited dispatchable range of thermal generators. There is an urgent need to expand the dispatchable range to promote the renewable energy accommodation. To this end, a new constraint relaxation-based day-ahead market mechanism is proposed. The trade-off between thermal units and renewable energy is achieved. Deep ramp offers and curtailment relief bids are proposed to express market participants' willingness for deep ramp and relieving curtailment. Thermal units submit their deep ramp offers to get compensation while renewable energy submits its curtailment relief bid to cut the energy price. Renewable energy is curtailed in the order of its curtailment relief bid. The model of constraint relaxation-based day-ahead market clearing procedure and model of equal curtailment without constraint relaxation are formulated, respectively. An accelerating technique combining Relaxation-based Neighborhood Search (RBNS) and Improved Relaxation Inducement (IRI) is introduced. A case study based on IEEE 118-bus system validates the effectiveness of the proposed mechanism and models and shows that the introduced accelerating technique can significantly reduce the computation time. • New constraint relaxation-based day-ahead market mechanism. • Promote the renewable energy accommodation. • Propose deep ramp offer and curtailment relief bid to achieve the trade-off. • Incent the thermal units to relax their constraints and deep ramp actively. • An accelerating technique is introduced to accelerate the solving process. [ABSTRACT FROM AUTHOR]

• This paper proposes a wide-area measurement-based data-driven approach for fault time determination and fault area location to avoid the influence of bad data. • The proposed approach has no requirement for impedance information compared with the existing impedance-based methods. • The proposed approach is verified via not only IEEE-39 bus system but also a practical provincial power grid of China. • An assistant software is developed using the proposed method to help dispatchers obtain fault information more accurately. This paper proposes a wide-area measurement-based data-driven approach for fault time determination and fault area location in the power system. To avoid the influence of bad data, the random matrix theory (RMT) is applied to the proposed approach. The measurements obtained from wide-area measurement system (WAMS) are used to form the raw data matrix, its mean spectral radius (MSR) index and the correlation among entries are employed to determine the fault time and locate the fault area using RMT. Since the proposed approach only requires the data obtained from WAMS and is a data-driven method, no physical model and topology information is needed compared with the existing model-based methods. Case studies are carried out on the IEEE 39-bus power system and a practical provincial power grid of China respectively, and a decision-support tool is programmed using the proposed method to help operators obtain fault information more timely and accurately. Simulation results show that the proposed method can determine the fault time precisely and locate the fault line, whether there is bad data in wide-area measurements or not. [ABSTRACT FROM AUTHOR]