Jimmy C.-H. Peng

Comprehensive Parameterization of Solar Cell: Improved Accuracy With Simulation Efficiency

Simulating photovoltaic (PV) power systems becomes important when studying integration issues of the intermittent solar energy into electric grids. The accuracy of the maximum power point (MPP) in PV output model is a concern since it represents the capacity of power generation. This paper proposes a comprehensive approach to identify PV cell parameters and avoid model errors of the MPP based on standard and simplified equivalent circuits. The estimation accuracy has been improved while maintaining the computational simplicity. This paper begins with the definition of the performance index used to quantify the model accuracy at the MPP. Based on the availability of information from the manufacturers datasheets, the accuracy and complexity of different equivalent circuits are discussed and investigated. The proposed approach has been successfully tested for solar cells made of mono and multi crystalline material.

Improved Recursive Electromechanical Oscillations Monitoring Scheme: A Novel Distributed Approach

This paper improves the existing Kalman-based technique for detecting electromechanical oscillations using Synchrophasor measurements. The novelty is the utilization of a distributed architecture to extract maximum a-posteriori (MAP) estimations of oscillatory parameters. This was achieved by an expectation maximization (EM) algorithm. To improve initial condition estimation, initial correlation information through a forward backward (FB) Kalman-like particle filter (KLPF) was integrated into the proposed scheme. Performance evaluation was conducted using IEEE New England 39-Bus system and Synchrophasor measurements collected from New Zealand Grid. The proposed method accurately extracted oscillatory parameters when the measurements were contaminated by continuous random small load fluctuations. The method also improved the capability of detecting multiple oscillations with similar frequencies.

Health Monitoring of Li-Ion Battery Systems: A Median Expectation Diagnosis Approach (MEDA)

The operations of Li-ion battery management system (BMS) are highly dependent on installed sensors. Malfunctions in sensors could lead to a deterioration in battery performance. This paper proposed an effective health monitoring scheme using a median expectation-based diagnosis approach (MEDA). MEDA calculates the median of a possible set of values, rather than taking their weighted average as in the case of a standard expected mean operator. Furthermore, a smoother was developed to capture important patterns in the estimation. The resulting filter was first derived using an one-dimensional (1-D) system example, where the iterative convergence of median-based proposed filter was proved. Performance evaluations were subsequently conducted by analyzing real-time measurements collected from Li-ion battery cells used in hybrid electric vehicles (HEV) and plug-in HEVs (PHEV) duty cycles. Results showed that the proposed filter was more effective and less sensitive to small sample size and curves with outliers.

Improved off-nominal operation of phasor measurement units using discrete fourier transformation

This paper presented a novel phasor estimator having less computational complexity and would be suitable for implementation in a digital signal processor (DSP). A discrete fourier transformation (DFT) using a fixed window and variable sampling time had been used to calculate phasor and frequency of the signal. The adjustment of the sampling time with the system frequency ensured a minimum leakage effect and accurate DFT computation. The phasor and frequency estimation was made at discrete intervals determined by the internal clock, which was synchronized with the 1 pulse per second (PPS) clock from the Global Positioning System (GPS). The bias corrected Jacobsen method was utilized for calculating the system frequency and its efficiency was compared with other three point DFT based frequency estimators. The proposed architecture of the DFT based phasor measurement unit was tested with signal corrupted with noise and harmonics, step and ramp variation of frequency and finally the amplitude jump conditions. The simulation results illustrated that the proposed system was able to capture the phasor at both steady state and dynamic conditions of the signal. IEEE standard C37.118.1-2011 was used as the benchmark for evaluating the proposed Phasor measurement unit (PMU).

Enhanced distributed estimation based on prior information

In this paper, a distributed estimation algorithm using Bayesian-based forward backward Kalman filter (KF) is proposed for stochastic singular linear systems. The method incorporates generalised versions of KF for bounded cases with complete and incomplete prior information, followed by estimation fusion of these cases. The incorporated filters remain optimal given the cross-covariance of the local estimates. The proposed approach is validated on a coupled-tank system.

An improved Empirical Mode Decomposition method for monitoring electromechanical oscillations

The use of Hilbert-Huang Transform (HHT) demonstrated to be effective in detecting time-varying electromechanical oscillations. HHT is a two-step algorithm, consisting of Empirical Mode Decomposition (EMD) and Hilbert Transform. EMD decomposes a signal into a set of Intrinsic Mode Functions, each containing the one oscillatory function. In this paper, the focus is on improving the EMD operation. The proposed enhancements increase the resistance of EMD against mode mixing. Mode mixing is defined as the intermittency of oscillatory dynamics due to operating conditions or abrupt disturbances. The improved EMD (IEMD) is comparatively evaluated with the conventional EMD (CEMD) for tracking simple synthetic signals and simulated system measurements. Based on observations, IEMD provides better mode tracking capability than CEMD.

Immunity Toward Data-Injection Attacks Using Multisensor Track Fusion-Based Model Prediction

Utilization of synchrophasor measurements for wide-area monitoring applications enables system operators to acquire real-time grid information. However, intentional injections of false synchrophasor measurements can potentially lead to inappropriate control actions, jeopardizing the security, and reliability of power transmission networks. To resolve this issue, a multisensor track-level fusion-based model prediction (TFMP) has been proposed. It has been demonstrated on a mature wide-area monitoring application, which detect electromechanical oscillations. In this paper, to extract the initial correlation information about attacked oscillation parameters, Kalman-like particle filter (KLPF)-based smoother has been used at each monitoring node. To reduce its computational burden, the KLPF-based smoother is diagonalized into subsystems. The scheme is further supported by the characteristics of moving horizon estimates for handling continuous load fluctuations and perturbations caused by data injections in power grids. Performance evaluations are conducted using different data-injection scenarios in the IEEE New England 39 Bus system. Results show the proposed TFMP accurately extracted oscillatory parameters from the contaminated measurements in the presence of multiple system disturbances and random data injections.

Improved Sample Value Adjustment for Synchrophasor Estimation at Off-Nominal Power System Conditions

The phasor measurement unit (PMU) commonly implements enhanced discrete Fourier transformation (EDFT) to compensate leakage and signal discontinuity errors at offnominal frequencies. However, EDFT results in a high total vector error (TVE) at large offnominal frequency and worsens with harmonics in power signals. Although the sample value adjustment (SVA) technique addressed such demerits, it generates an asymmetrical TVE profile. In addition, the frequency estimator was not taken into account in the previous publication on SVA. This paper proposes an improved sample value adjustment (ISVA) as a pre-DFT technique to further enhance the phasor estimation. The ISVA generates a symmetrical TVE profile and performed better than EDFT, SVA, and Taylor weighted least-square method at steady-state conditions. Subsequently, the ISVA is integrated with a recursive least-square frequency estimator, which enabled it to track dynamic signals as well. The algorithm is validated in MATLAB simulation and in a prototyped PMU. Static and dynamic test signals are based on the IEEE C37.118.1a-2014 standard.

Improved deterministic real-time estimation of Maximum Power Point in photovoltaic power systems

Heuristic tracking algorithms are commonly adopted for Maximum Power Point Tracking (MPPT), which generates oscillations around the Maximum Power Point (MPP) of the PV array. Alternative solution using deterministic curve-fitting approach is proposed in this paper to locate the MPP accurately. The non-linear PV curve is approximated by a polynomial equation, of which the coefficients can be identified in real time by using a recursive methodology with variable forgetting factors. This method is named as Recursive Estimation with Variable Forgetting Factor (REVFF). Thus, the tracking accuracy is improved without the perturbation of PV voltage. The proposed system and control scheme are implemented and verified by simulation.

Decentralized sliding mode control for load frequency problem in three - Area power systems

Frequency issues exist in generation, transmission and load sectors. From the load point of view, frequency sensitive devices like engines or clocks require quasi constant frequency to work properly. Whereas in the transmission system, power losses like hysteresis and eddy currents in transformers are frequency dependent. At the generation side, it is essential to prevent rotor angle from exceeding maximal threshold value; thus avoiding generators becoming out-of-step. Many techniques and model predictive control were published to solve the load frequency control (LFC) problem. Moreover, current optimization methods and high performance computers allow engineers to optimize complicated linear and nonlinear problems within reasonable time. Among optimization techniques, genetic algorithm (GA) optimization is utilized by control designers. It is also supported by Optimtool - MATLAB toolbox. This paper presented a comparative study of conventional and sliding mode control (SMC) designs for LFC. Optimized conventional controllers (PI and PID) were applied into a three - area system. Generated responses from conventional controllers were compared to responses from systems equipped with decentralized SMC.