An ANN-based load model for fast transient stability calculations

Power load is an important element in power system. While, the traditional load model is fixed. So, when the load components change, the load model will be inappropriate. Based on the Multiple Model Theory, this paper proposes the Adaptive Synthesis Dynamic Load Model (ASDLM). In load modeling, the load model set is established, and every model in the model set has a weight. The time-varying classical load model (TVCLM) is proposed as the basic structure of the load models in load model set. The parameters and weights of the power load model can be adjusted when there is a maneuvering in the actual load system. Then, the models in the model set are integrated to ASDLM. The load data fitting results of ASDLM verify the adaptability and practicability of the proposed model structure and modeling ideology.

Owing to the characteristics of nonlinearity, asymmetry and stochastic volatility, electrified railway traction loads have arose some hazardous effects to security and stability, reactive power compensation and voltage control, power supply quality of power system. It has practical significance to research on the influence of traction loads to power system for ensuring security and stability of power system. By using Power System Analysis Software Package/User Program Interface (PSASP/UPI) function, the electrified railway traction measurement-based load model (traction load model, TLM) for transient stability simulation are established, and then the TLM is applied to Hunan Electrical Power Network. The dynamic characteristics of TLM and the difference characteristics between TLM and the traditional load model are analyzed through setting different faults. The paper provides a convenient reference for further research on the relay protection setting calculation of power system and safety controlling strategies of traction power supply system.

The time-varying parameter identification of load models has attracted broadly attention when large amounts of intermittent distributed generations (DGs) and stochastic loads are integrated in active distribution network (ADN). However, in traditional time-varying parameter identification approaches, the load model is usually developed with the composite load model (CLM) or synthesis load model (SLM), which are not suitable for representing the dynamics of DGs. Moreover, the plateau phenomenon and the continuous low-quality data further reduce the performance of traditional load models with time-varying parameter. Therefore, to address these issues, a novel time-varying parameter identification approach with extended Kalman filter (EKF) is designed for the load modeling. To represent the behavior of modern loads, an improved load model contains a parallel SLM and voltage source converter (VSC) is developed. Then, the target parameters, whose changes produce larger variations in model outputs, are selected with trajectory sensitivity to avoid plateau phenomenon. Also, the Chi-square test and the proposed weighted suppression strategy are utilized to suppress the continuous low-quality data. The simulation results on a system-level ADN model show that the proposed approach could accurately identify the parameters of time-varying load models.

With the addition of new distributed energy sources, a new and more accurate load model need to be established. Based on MATLAB/Simulink, this paper researches on the modeling and simulating of photovoltaic power generation system and battery energy storage and their equivalent model. First, a detailed model of the photovoltaic system is built. Boost circuit and MPPT control are selected on the DC side of photovoltaic power generation system. The double closed loop control method of current internal loop and external voltage loop is used for grid inverter control. The model is improved considering the capability of LVRT (low voltage ride through). Based on the above precise models, the model is equaled. The equivalent transfer function is calculated by the grid connected control loop, so the model is simplified. The battery energy storage module establishes a general mathematical model based on the charging and discharging principle. Charge and discharge are controlled through the DC/DC converter. And it is connected to the power grid after inverting and filtering. The common PQ control are selected in the inverter control mode. According to the same principle, the energy storage model is equaled. Finally, based on the traditional load model, the above load model is added to set up a new generalized integrated load model. And the actual model is replaced to build up an equivalent model. The equivalent model is compared with the actual model to verify the equivalent description ability.

Effect of load models on assessment of energy losses in distributed generation planning

Efficient identification of critical load model parameters affecting transient stability

The behavior of the load model in power system stability studies has been shown by various investigators to be a very important factor in determining the stability of power system elements. Load models which are presently available in typical stability programs make it difficult if not impossible to model the actual dynamics of power system load. Present load models allow easy static representations, but require detailed individual dynamic models such as induction or synchronous motor models. Thus a simple load model that can represent power system composite load dynamics will be a valuable tool in the stability study Presented herein is a second order state space load model which can represent power system composite load dynamics. In addition, the parameters of this state space load model can be identified from actual system load measurements by a weighted least squares parameter identification process. Several authors recognized the significance of load behavior to changes in voltage as long as 25 years ago. However, most of the attention in stability studies has been directed toward improved generator and control system models. Until recently, little modeling has been considered necessary for power system loads since the effects of load models in stability studies were considered only secondary. At present the load representation in typical stability programs is limited to a static load model where the load is represented as constant MVA, constant current, constant impedance, or some combination of the three.

This paper presents the results of composite load model validation based on the distribution-level micro- phasor measurement unit (PMU) measurements. The actual micro-PMU measurements from the distribution feeders are collected by Lawrence Berkley National Laboratory (LBNL). The composite load model parameters are initially generated using the load model data tool developed by Pacific Northwest National Laboratory (PNNL). The generated load models are calibrated to match the performances with the micro-PMU measurements using disturbance records “play-in” capabilities available in GE PSLF simulation package. A trial-and-error approach based on engineering judgement is proposed in this paper to benchmark the generated load model parameters and is implemented in the prototype of load model validation tool, which would be a supplement to the load model data tool.

The energy transition poses a challenge for the electricity distribution network design as new energy technologies cause increasing and uncertain network loads. Traditional static load models cannot cope with the stochastic nature of this new technology adoption. Furthermore, traditional nonlinear power methods have difficulty evaluating very large networks with millions of cables, because they are computationally expensive. This paper proposes a method which uses copulas for modeling the uncertainty of technology adoption and load profiles, and combines it with a fast linear load flow model. The copulas are able to accurately model the stochastic behavior of solar irradiance, load measurements, and mobility data, converting them into electricity load profiles. The linear load flow model has better scalability and stability compared to traditional load flow models. The models are applied to a case study which uses a real-world dataset consisting of a realistic technology adoption scenario and a low-voltage network with millions of cables, which considers both voltage and current problems. Results show that risk profiles can be generated for all cables in the network, resulting in a valuable map for the district network operator as to where to focus their efforts.

The estimation of optimum markup percentage is a critical activity for a contractor to win the tender. It is affected by many factors. This paper presents a novel method of markup estimation combining rough sets (RS) theory and back-propagation (BP) network for construction project. RS theory is utilized as a preprocessor to delete the redundant irrelevant factors to the project markup. Then the relevant factors are used to train the BP network and predict the project markup. Actual prediction results show that the performance of RSBP model combing RS theory and BP model is superior to that of BP network with higher global convergence ability and higher computing speed. In addition, the mean relative error of RSBP model is also smaller than the BP model.

Modelling of voltage dependent load is always a current topic due to existence of an adequate load model is a prerequisite for calculation of steady states and the analysis of transient regimes of power systems. Given that it is important to know the load model parameters, we have performed the measurements in electric power system of Bosnia and Herzegovina (EPS BIH). The main idea is to identify the values of voltage parameters of dynamic load model. Therefore, this paper describes the dynamic load models and the methodology for exponential dynamic load model parameter identification. It also presents the voltage parameters of dynamic load model for a node in EPS BIH and compares them to similar published studies, concluding that the difference in given parameter values is the consequence of local load characteristics.

In this paper, a novel load model structure considering multi-level low-voltage release characteristics is proposed. In the proposed model, three-level low-voltage release characteristic parameters are introduced into the existing synthesis load model (SLM) to simulate the load low-voltage release characteristics during disturbance. Next a load model parameter calculation method considering load multilevel low-voltage release characteristics is proposed. Calculating the per-unit value of a single device type real load power relative to the total load node real power, and using the per-unit value as a weighted factor for polymerization, the each stage action voltage and low-voltage release average delay time constant in the synthesis load of low-voltage multi-level release characteristic parameters can be derived. Then based on the percentage of actual load losses after disturbance, multilevel low-voltage release proportion parameters is determined by fault fitting method. These three parameters can be further revised iteratively to make the error between actual load loss and calculation load loss to be minimum. Thus the low-voltage multi-level release characteristics of the load is accurately simulated. The practical calculation case of Shanghai power grid shows that low -voltage multi-level release characteristic parameters generated by the proposed load model structure and the proposed method for determining the load model parameters, can well describe the load low-voltage multi-level release characteristics.

Load parameter identification is one of the keys and difficult points in power system analysis and research. With the rapid development of the grid scale, the load structure is becoming more and more complex, and the adaptability of the load model structure and typical parameters to the actual power grid has become increasingly prominent. In response to this problem, a parameter identification method for power grid load models in the internet of things (IoT) environment is proposed. Firstly, a load model is established under the power IoT “cloud-network-edge-terminal” architecture. Secondly, the genetic algorithm (GA) is used to identify the parameters of the dynamic load model in the edge layer. At the same time, without changing the structure of the load model, a load parameter security protection method based on edge computing is proposed to ensure the safety and effectiveness of the load parameter identification results. Finally, the effectiveness of the proposed load parameter identification method is tested by comparing response time, load parameter identification accuracy, and edge computing loss.

Transient stability may be seriously affected when a large number of distributed generators (DG) stop simultaneously during voltage sag. It is necessary to analyze accurately the dynamics of bulk power systems with high DG penetration. In this paper, transient stability is studied by analyzing power-angle curves of generators while considering load dynamics and model order reduction at lower voltages. Based on the analysis, a decrease in the load internal resistance after voltage sag causes transient instability of generators. The phenomenon is confirmed through simulation using a one-machine and one-load model. This paper also suggests that the simulation results might be misled by traditional bulk power system modeling such as using the static load model and ignoring impedance at lower voltages. As for the numerical simulation, a large level of DG stoppage leads to transient step out in a bulk power system, and the stability is greatly improved by DG voltage regulation. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(2): 20– 29, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20608

This paper discusses the development of a load component database for household appliances and office equipment. To develop more accurate load models at both the transmission and distribution levels, a better understanding of the behaviors of home appliances and office equipment associated with the variations of the power system voltage becomes more and more critical. The Bonneville Power Administration (BPA) has performed a series of voltage tests against home appliances and office equipment since 2005. Since 2006, researchers at Pacific Northwest National Laboratory have collaborated with BPA personnel and developed a load component database based on the appliance testing results to facilitate load modeling work for the Western Electricity Coordinating Council (WECC). In this paper, the testing procedure and results are first presented. Then, the load model parameters are derived and grouped. Recommendations are given for aggregating the individual appliance models to the feeder level, the models of which are used for distribution and transmission level studies.