Measurement-based exponential recovery load model: development and validation

A methodology for measurement-based load modeling based on integrating dynamic models is presented in this paper. A composite load model (CLM) and an exponential recovery load (ERL) model are combined and used as a model structure for load representation coupled with a heuristic criterion. An improved particle swarm optimization (IPSO) algorithm that considers a speed update modification to enhance its global search capabilities is used to identify the model's parameters. Several simulations are performed in two test systems, using load response measurements after small and severe disturbances, considering noise measurement to simulate monitoring devices. Results verify that the proposed load model has a better performance than the individual load models, and also IPSO algorithm is adequate for parameter identification.

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model’s ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification.

In this study, the problem of simultaneous and coordinated tuning method for thyristor-controlled series capacitor (TCSC) power oscillation damping controllers and conventional power system stabilizer (PSS) controllers in multi-machine power systems is considered. For a multi-machine power system, the tuning of the PSS and TCSC controllers is generally formulated as an objective function with constraints consisting of the damping ratio and damping factor. Using the linearized system model and the parameter-constrained nonlinear optimization algorithm, interactions among TCSC controller and PSS controllers are minimized. It is done by simultaneously optimizing, the parameters of the damping controllers using sequential quadratic programming technique. Simulation results of a two area multi-machine power system validate the effectiveness of the approach and its capacity in tuning multiple controllers in power systems simultaneously.

This paper describes a practical application of the Expectation Maximization algorithm for computing the static load model of large electricity consumers. Such a model is necessary for effective and reliable operational control of electric power systems. The possibility of estimating basic states of electrical load using arrays of measured voltage and power data is shown. The criteria for selecting the most appropriate clusters are formulated and a step by step linear load model parameter estimation method is proposed. A practical method application is illustrated by using measurements of voltage and real power obtained at a large industrial facility. As a result, a load model with two basic states and corresponding linear models is obtained.

This paper proposes a novel current injection model of Pulse width Modulation based Series Compensator (PWMSC), as new FACTS controller, for damping of low frequency oscillations. The PWMSC operates as a means of continuous control of the degree of series compensation through the variation of the duty cycle of a train of fixed frequency-pulses. The methodology is tested on the sample single machine power system including PWMSC controller by performing computer simulations for small and large distributions. MATLAB/ Simulink software package was used for the simulations.

Accurate modeling and dynamic simulation of integrated AC/DC grids play a fundamental role in power system stability analysis and control. Unbalanced operating conditions in AC side results in a large number of second harmonic current or voltage distortion on DC side and third harmonic on AC side. In this work, we build an advanced dynamic phasor model for the LCC-HVDC converter, which is suitable for the normal operating and unbalanced fault conditions of the system. Meanwhile, a second-order dynamic phasor model is proposed for the control system modeling and DC line. Finally, the proposed models are applied to the first CIGRE HVDC benchmark system to test the new model's validity. The simulation results are validated by PSCAD/EMTDC.

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 describes a method of identifying the transient parameters of a generator under normal operating conditions using on-line measurements of the generator current output or the voltage of the bus to which the generator is connected through a transformer. A decoupled method is developed to identify the parameters of the generator in any power system and under any operating conditions. The method is based on Park's two-axis model, and the parameters to be estimated have definite physical meanings, which are suitable for stability analysis and control of power systems. The continuous corrected extended Kalman filter is used as the identification algorithm, so the model does not need to be changed into a discrete form. The identification converges to within 1% of the actual parameter values in most power system structures.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, a generic load model is presented, suitable for dynamic simulations over a wide range of loading conditions. The proposed model is based on the well-established exponential recovery load model. Contrary to the conventional first-order form of this model, the proposed formulation employs variable-order transfer functions, enabling the accurate simulation of high-order load dynamics. A complete methodology is proposed for the identification of the required load model parameters from real field measurements. For this purpose, the Vector Fitting technique is introduced for the first time as a method for measurement-based load modeling. Several simulation scenarios are performed using the NEPLAN software to validate the accuracy of the proposed model. Numerical studies indicate that the developed load model captures accurately the load dynamic behavior over a wide range of loading conditions.

Direct stability analysis using an energy function has recently been widely used for angular and voltage stability analysis in electric power systems. Considerable efforts have concentrated on seeking energy functions for lossy power systems, accomplished only for the simple two-bus system. A new approach is presented to derive an energy function by using the complex integral of bus current equations with respect to bus voltages. Under an assumption that all transmission lines have a uniform R/X ratio, it is shown that the energy function can be developed for multimachine power systems with losses. The proposed energy function is tested on sample systems by comparing the time simulation method, which shows the validity of the proposed method.

A new decomposition-aggregation approach is developed to perform transient stability analysis of an N-machine power system with a speed governor. The system decomposition is performed so that every subsystem includes four machines, instead of only two or three machines as considered in other work. The system model is derived and decomposed into (N − 1)/3 11th-order interconnected subsystems considering transfer conductances, nonuniform mechanical damping, electromagnetic damping and speed governor action. Each of these subsystems is decomposed into a free subsystem containing 12 nonlinearities and interconnections. A square aggregation matrix of order (N − 1)/3 is obtained instead of orders N and (N + 1)/2 for the pair-wise and triple-wise decompositions, respectively. The approach is applied to a 10-machine power system and an estimate for the system asymptotic stability domain is determined. System transient stability computations are carried out considering a three-phase short circuit fault, with successful reclosure, near a system bus or a sudden loss of one of the system loads. The approach is used to determine a reclosure time for the faulted line and a reconnection time for the lost load required for the system to regain its prefault (normal) conditions. The approach developed is suitable and can be easily used for stability analysis of power systems (the number of machines may be more than 10). The developed approach can also reduce the conservativeness of the decomposition-aggregation method.

It is becoming increasingly difficult to conduct standard stability studies on modern power systems due to the growing use of power electronic devices. Power electronic converters, regardless of their power or voltage, must contend with a rising challenge known as electromagnetic transient stability. For power system stability analysis and control, transient stability assessment (TSA) is essential. On the basis of numerical integration of the dynamics equations for an EPS under consideration at a particular disturbance, a method for assessing transient stability reserves is described. In order to improve the transient stability of hybrid AC/DC power systems, a multi-HVDC emergency coordinated modulating method has been suggested in this study. Multi-DC lines are used to build and test the AC/DC hybrid power system. Machine learning models that forecast power system transient stability are more reliable, versatile, and robust if they incorporate knowledge of power system dynamics into their data gathering process. Fault location and how disturbance effects spread over the network are critical for determining unstable regions of operation, thus this determination is made using domain knowledge. That’s why traditional machine learning models may now be used in an instance transfer learning framework thanks to a whole new data gathering technique. Therefore, the suggested solution does not rely primarily on the collected data, but also incorporates system insights.

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.

A single machine-infinite-bus (SMIB) system including the interline power flow controllers (IPFCs) and the power system stabilizer (PSS) controller is addressed. The linearized system model is considered for investigating the interactions among IPFC and PSS controllers. To improve the stability of whole system again different disturbances, a lead-lag controller is considered to produce supplementary signal. The proposed supplementary controller is implemented to improve the damping of the power system low frequency oscillations (LFOs). Imperialist optimization algorithm (ICA) and shuffled frog leaping algorithm (SFLA) are implemented to search for optimal supplementary controllers and PSS parameters. Moreover, singular value decomposition (SVD) method is utilized to select the most effective damping control signal of IPFC lead-lag controllers. To evaluate the system performance, different operating conditions are considered. Reponses of system in five modes including uncoordinated and coordinated modes of IPFC and PSS using ICA and SFLA are studied and compared. Considering the results, response of system without controller shows the highest overshoot and the longest settling time for rotor angel at the different operating conditions. In this mode of system, rotor speed has the highest overshoot. Rotor angel in the system with only PSS includes lower overshoot and oscillation than system without controller. When PSS is only implemented, rotor speed deviation has the longest settling time. Rotor speed deviation in the uncoordinated mode of IPFC and PSS shows lower overshoot than system with only PSS and without controller. It is noticeable that in this mode, rotor angel has higher overshoot than system with only PSS. The superiority of the suggested ICA-based coordinated controllers is obvious compared with SFLA-based coordinated controllers and other system modes. Responses of coordinated PSS and IPFC SFLA-based supplementary controllers include higher peak amplitude and longer settling time compared with coordinated IPFC and PSS ICA-based controllers. This comparison shows that overshoots, undershoots and the settling times are reduced considerably in coordinated mode of IPFC based controller and PSS using ICA. Analysis of the system performance shows that the proposed method has excellent response to different faults in power system.

Several techniques have been developed to estimate the load parameters in power systems. Most of the algorithms mainly focus on estimating the parameters for offline studies. With ongoing smart grid development, high-resolution data at fast rates are available to allow load modeling in real-time. This paper addresses the challenges in online estimation of the load parameters using Phasor Measurement Unit (PMU) data. A novel adaptive search-based algorithm to estimate load model parameters is presented here. In this paper, Z (constant impedance), I (constant current) and P (constant power), ZIP load model is derived. Simulation results for ZIP parameter estimation are presented using the IEEE 14-bus system. Simulation results demonstrate the accurate estimation of the ZIP load model. ZIP parameters are also estimated using the industrial data and the voltage stability limits are computed using actual data to show the impact of ZIP parameters versus the traditional constant power load model.