Load Model Parameter Identification Research Articles

Parameter identification method of load modeling based on improved dung beetle optimizer algorithm

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.

A Novel Method for Parameter Identification of Renewable Energy Resources based on Quantum Particle Swarm–Extreme Learning Machine

Accurately determining load model parameters is of the utmost importance for conducting power system simulation analysis and designing effective control strategies. Measurement-based approaches are commonly employed to identify load model parameters that closely reflect the actual operating conditions. However, these methods typically rely on iterative parameter search processes, which can be time-consuming, particularly when dealing with complex models. To address this challenge, this paper introduces a parameter identification method for the generalized synthetic load model (SLM) using the Extreme Learning Machine (ELM) technique, with the aim of enhancing computational efficiency. Furthermore, to achieve better alignment with load response curves, a Quantum Particle Swarm Optimization (QPSO) algorithm is adopted to train the ELM model. The proposed QPSO-ELM-based SLM parameter identification method is subsequently evaluated using a standard test system. To assess its effectiveness, parameter sensitivity analysis is performed, and simulation results are analyzed. The findings demonstrate that the proposed method yields favorable outcomes, offering improved computation efficiency in load model parameter identification tasks.

Traction Load Modeling and Parameter Identification Based on Improved Sparrow Search Algorithm

In this paper, a traction load model parameter identification method based on the improved sparrow search algorithm (ISSA) is proposed. According to the load characteristics of the AC traction power supply system under transient disturbance, the model structure of the traction load is equated to the composite load model structure of the static load shunt induction motor’s dynamic load. The traditional sparrow search algorithm is improved to enhance its accuracy and convergence. The generalization ability of the model was tested, and the accuracy of the proposed model was verified. Using the ISSA to determine the load model from the measured data, the results can verify the effectiveness of the ISSA for comprehensive load model parameter identification. Comparing the ISSA with the traditional SSA and PSO algorithms, it shows that the ISSA has better accuracy and convergence.

Data improvement for ambient signal based load modeling via wide-area state estimation

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.

A Fast Parameter Identification Method for Composite Load Model Based on Jumping and Steady-State Points of Measured Data

The classic measurement-based method for load model parameter identification relies on lots of transient simulations and optimization iterations, which is computationally intensive, and is unsuitable to the terminal devices for recording load characteristic to participate in grid edge computing. To solve this difficulty, a fast identification method for load model parameters based on jumping and steady-state points of measured data is proposed, which greatly reduces the calculation time by avoiding the transient simulations and random optimizations of the classic method, and at the same time, well retains the accuracy of identified parameters. Firstly, the method extracts four points of measured data as calculation points, i.e., the point after voltage sag, two points before and after voltage recovery, and the final steady-state point. Then, the method calculates the state variables and powers at the four points through steady-state calculation, implicit trapezoid integration method and Hermite–Simpson method respectively. Finally, according to the measured powers of the four points, the method provides an initial key load model parameters through polynomial approximation method and finds the optimal parameters through Nelder-Mead algorithm. The accuracy and practicality of the method are demonstrated by test and field case studies, and the computation burden is less than 2.5% of that in the classic method without obvious loss of identification accuracy.

Development and application of measurement data pre-processing tools for parameter identification considering q-axis voltage

At present, when model parameter identification is carried out, measurement data from phase measurement units or fault recorders are generally used directly. These two types of devices can directly provide the fundamental positive sequence quantities required for parameter identification, but cannot output the dq components. If these measurement data can be fully utilized for parameter identification, it is very beneficial to improve the model accuracy. In this paper, according to the engineering needs of load model parameter identification, the extraction method and variation law of dq components are studied, and the data pre-processing tool is developed and put into use.

Recent developments in load model parameter identification via ambient PMU signal

Load modelling has a significant impact on operation mode arrangement, transient stability analysis, small disturbance stability calculation and other aspects in power system. It is an important basis and issue for the decision-making of dispatching operation department. As a novel research field of measurement-based identification method, load model parameter identification via ambient PMU signal has gained some developments. This article reviews the current research status of ambient data-based load parameter identification, outlines its basic principle and typical framework, and expounds three technical routes. By summarizing the characteristics of various methods, the future research direction is prospected.

Technical Support System for Power System Load Modeling

Background: In order to better establish accurate load models and meet the practical demand of current power system load modeling, it is necessary to establish related technical support systems for power system load modeling. Objective: The purpose of the paper was to construct the overall scheme of power system load modeling technology support system and complete the development of the system. Methods: Based on the modular design idea, the system adopts a multi-level architecture combining B/S and C/S modes, covering the key technologies of substation classification based on selforganizing neural network algorithm, load dynamic characteristic classification based on lifting wavelet packet algorithm, load model parameter identification and load modeling based on adaptive interactive multiple model (AIMM) algorithm. Results: After actual operation verification, the built technology support system can well solve the related problems of substation classification, load dynamic characteristic classification, load model parameter identification and load modeling. It has the characteristics of a friendly man-machine interface, simple operation and strong extensibility. Conclusion: The built technology support system provides powerful technical support for improving the load data management level of the power system and establishing an accurate load model, and promotes the practical process of load modeling theory.

A two-stage framework for ambient signal based load model parameter identification

Load modeling is a crucial part of modeling a power system. Traditional load model identification methods are mainly based on post large disturbance response, which cannot be conducted without large disturbance events. In this paper, a new approach of ambient signals based load model parameter identification is proposed to solve this problem, through which the time-varying load model parameters can be identified more frequently. First, the composite load model structure and the measurement based identification approach are introduced. Then, a two-stage identification framework to identify the load model parameters from ambient signals is proposed. In the first stage, the electromagnetic parameters are identified using an optimization method, with the predicted error as the objective function and the differential evolution as the optimization algorithm. In the second stage, the mechanical parameters are identified using linear regression. Finally, the effectiveness of the proposed identification method is validated through the simulation results in Guangdong Power Grid with practically measured ambient signals data. The proposed two-stage identification framework has shown its ability to accurately identify the load model parameters from ambient signals and the advantage over the other identification approaches.

Applicability comparison of different algorithms for ambient signal based load model parameter identification

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.

Parameter Identification of Comprehensive Load Modeling Based on Improved Genetic Algorithm

As the intelligent analysis and calculation of information developing very fast, the algorithms for load model parameter identification are growing fast the same time, these algorithms were aimed at quick convergence, improving calculation accuracy and so on, making the identification for load model parameter being more and more fast and accurate. This paper provides an improved GA with adaptive ability to identify the parameter of comprehensive load modeling. Choosing TVA comprehensive load model as the computational model, and use the improved GA given by this paper to identify the parameter of the model. The computer simulation result shows that the improved GA proposed by this paper has improved the calculation effect, especially has palpable effects on accelerating convergence and shorten the time of identification.

Two‐step method for the online parameter identification of a new simplified composite load model

The electrical load is one of the most important parts of power systems. Parameter identification of load model is critical for power system stability analysis. The composite load model, which comprises a static load and a dynamic load, is one of the most widely used models in power system simulation and control. As the load characteristics could change considerably over time, online parameter identification is needed. However, because of the non-linearity and complexity, online parameter identification for load models remains difficult to achieve, and there is rarely an effective solution. This study proposes a load model simplification and parameter identification method to solve this problem. First, the composite load model is preliminarily simplified by choosing the dominant parameters. Next, the load model is further simplified based on a second-ordered state equation of the induction motor. Subsequently, a two-step method for online parameter identification is presented. The first step is the electrical parameter identification based on the multi-layer searching method proposed in this study. The second step is the mechanical parameter identification via the Newton method. Finally, an example for a typical case system is presented to demonstrate the effectiveness, efficiency and accuracy of the two-step method.

Feature extraction and classification of load dynamic characteristics based on lifting wavelet packet transform in power system load modeling

Load dynamic characteristics classification and synthesis is the main approach to solve the problem of load time-variation. The basis and prerequisite of load dynamic characteristics classification is load dynamic characteristics feature extraction. Load model parameter space or the model response space gained by a standard voltage excitation is usually selected as the feature vector space in current practice of load dynamic characteristics feature extraction. However, both methods need to determine the load model structure and identify the model parameters. It would increase not only calculation error but also calculation time in the process of load model structure determination and parameter identification. Then the accuracy of the final classification results would be affected. It is reasonable and scientific to extract feature vector space of load dynamic characteristics directly from the measured response space. In this paper, a feature extraction method based on lifting wavelet packet transform is proposed for load dynamic characteristics classification. The load measured current response data is decomposed and reconstructed, then the wavelet packet coefficients can be extracted to construct energy moment based feature vector. On this basis, the load dynamic characteristics classification can be realized using fuzzy c-means (FCM) method. Finally, the validity and practicality of the proposed method have been proved by feature extraction and classification of dynamic simulation data acquired using Matlab/Simulink and field measurement data. Compared with traditional wavelet packet transform, the lifting wavelet packet transform has shown advantages both in computational speed and reconstruction error and can improve the accuracy of load dynamic characteristics classification.

Research on Power Load Modeling Based on Improved Perturbed Method

This paper introduces a conventional synthesis power load model considering distribution network in PSASP, through analyzing the model structure and achieving process, proposes a power load model parameter identification method based on improved perturbed method. The paper gives parameters identification method of complete synthesis load model, including reactive power compensator parameters. The parameters identification method combines parameter identification process with sensitivity analyzing process together, not only can get model parameters but also can obtain the sensitivity of all parameters, which saves computing time and reduces the amount of calculation, the final simulation results prove the method is effective and feasible. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.5113

Comparsion of Two Kinds of Optimization Algorithm for Load Model Parameter Identification

Parameter Identification is the key technology in measurement-based load modeling. In order to identify parameters in power system ,the differential method which is based on the multiple curves fitting and interpolated method are compared in the paper. Numerical results illustrate that the differential method can improve the accuracy for load modeling parameter identifications.