Qi Li, Yifan Wang^*, Yuxi Liu, Xingjing She, Yixuan Wu

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074066 - 29 January 2026

Abstract In data communication, limited communication resources often lead to measurement bias, which adversely affects subsequent system estimation if not effectively handled. This paper proposes a novel bias calibration algorithm under communication constraints to achieve accurate system states of the interested system. An output-based event-triggered scheme is first employed to alleviate transmission burden. Accounting for the limited-communication-induced measurement bias, a novel bias calibration algorithm following the Kalman filtering line is developed to restrain the effect of the measurement bias on system estimation, thereby achieving accurate system state estimates. Subsequently, the Field Programmable Gate Array (FPGA) implementation More >

Fangju Zhou¹, Hanbo Zhang², Na Ye¹, Jing Huang¹, Zhu Ren^1,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4539-4561, 2025, DOI:10.32604/cmc.2025.065946 - 30 July 2025

Abstract This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and a detector. When measurements are transmitted via wireless networks to a remote estimator, the innovation sequence becomes susceptible to interception and manipulation by adversaries. We consider a class of linear deception attacks, wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector. Given the inherent volatility of the detection function based on the detector, we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution… More >

Hao Jiao¹, Xinyu Liu², Chen Wu¹, Chunlei Xu¹, Zhijun Zhou³, Ye Chen³, Guoqiang Sun^2,*

Energy Engineering, Vol.122, No.7, pp. 2977-2992, 2025, DOI:10.32604/ee.2025.064206 - 27 June 2025

Abstract With the continuous expansion of the power system scale and the increasing complexity of operational mode, the interaction between transmission and distribution systems is becoming more and more significant, placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods, this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification. To resolve the scalability issue of coupled transmission and distribution power systems, clustering… More >

Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

Bo Yang^1,*, Yucun Qian¹, Jianzhong Xu², Yaxing Ren³, Yixuan Chen⁴

Energy Engineering, Vol.121, No.8, pp. 2085-2091, 2024, DOI:10.32604/ee.2024.050083 - 19 July 2024

Abstract This article has no abstract. More >

Shunyi Zhao^1,*, Xiaoli Luan¹, Jinfeng Liu², Ruomu Tan³

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 1-3, 2023, DOI:10.32604/cmes.2022.024993 - 29 September 2022

Abstract This article has no abstract. More >

Wentao Liu, Junxia Ma, Weili Xiong^*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 873-892, 2023, DOI:10.32604/cmes.2022.020565 - 31 August 2022

Abstract This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space models in the presence of disturbances. A bilinear state observer is designed for deriving identification algorithms to estimate the state variables using the input-output data. Based on the bilinear state observer, a novel gradient iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous mixed p-norm cost function. The gain at each iterative step adapts to the data quality so that the algorithm has good robustness to the noise disturbance. Furthermore, to More >

Ziwei Jiang¹, Shuaibing Li^1,*, Xiping Ma², Xingmin Li², Yongqiang Kang¹, Hongwei Li³, Haiying Dong¹

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 295-317, 2022, DOI:10.32604/cmes.2022.019996 - 02 June 2022

Abstract

The development and utilization of large-scale distributed power generation and the increase of impact loads represented by electric locomotives and new energy electric vehicles have brought great challenges to the stable operation of the regional power grid. To improve the prediction accuracy of power systems with source-load two-terminal uncertainties, an adaptive cubature Kalman filter algorithm based on improved initial noise covariance matrix Q0 is proposed in this paper. In the algorithm, the Q0 is used to offset the modeling error, and solves the problem of large voltage amplitude and phase fluctuation of the source-load two-terminal uncertain systems.

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Min Liu^*

Energy Engineering, Vol.119, No.2, pp. 781-800, 2022, DOI:10.32604/ee.2022.016657 - 24 January 2022

Abstract With the application of phasor measurement units (PMU) in the distribution system, it is expected that the performance of the distribution system state estimation can be improved obviously with the PMU measurements into consideration. How to appropriately place the PMUs in the distribution is therefore become an important issue due to the economical consideration. According to the concept of efficient frontier, a value-at-risk based approach is proposed to make optimal placement of PMU taking account of the uncertainty of measure errors, statistical characteristics of the pseudo measurements, and reliability of the measurement instrument. The reasonability More >

Yonghua Zhou¹, Xun Yang¹, Chao Mi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.89, No.6, pp. 497-512, 2012, DOI:10.3970/cmes.2012.089.497

Abstract The movements of vehicles equipped with various positioning systems such as global and wireless positioning ones have provided beneficial channels to acquire abundant traffic flow information for total road network. However, not all vehicles are mounted with positioning systems and not all equipped positioning facilities are always active. This paper will address how to estimate the number and the states of unequipped vehicles through a series of observations on equipped ones. The proposed estimation process initiates employing the non-analytical microscopic traffic model for particle filter to estimate the number, positions and speeds of unequipped vehicles… More >