Utility harmonic impedance is an important parameter for harmonic mitigation. In this paper, a method for utility harmonic impedance estimation method based on constrained independent component analysis is proposed. The conventional impedance estimation method based on ComplexICA has two major problems: the algorithm is not suitable for separating weak and strong source mixed signals, and lots of sample data should be provided to avoid converging on a local optimum. To solve the two problems, the prior information of the utility harmonic source is added to the objective function of ComplexICA; in this paper, the measurement data at PCC when the load is shutdown are chosen as the prior information. Then the utility harmonic source signal can be recovered and the separated matrix can be obtained effectively. The connection between the utility harmonic source, utility harmonic impedance and the data at PCC are established using Norton equivalent circuit, and then the separation matrix is used to calculate utility harmonic impedance. The performance and feasibility of the proposed method are verified by the computer simulation and field test. Compared with the current ComplexICA method, the proposed method is more adaptive to changes in the background harmonic and the calculation result is more stable.

Harmonic sources detection at the point of common coupling is an essential step for harmonic contribution determination and harmonic mitigation. The harmonic distortion power rate index is proposed for harmonic source location based on IEEE Std 1459-2010 in the paper. The method only based on harmonic distortion power is not suitable when the background harmonic is large. To solve this problem, a threshold is determined by the prior information, when the harmonic distortion power is larger than the threshold, the customer side is considered as the main harmonic source, otherwise, the utility side is. A simple model of public power system was built in MATLAB/Simulink and field test results of typical harmonic loads verified the effectiveness of proposed method.

The integration of large-scale power electronic equipment has intensified harmonic issues in power systems. Accurate harmonic models are fundamental for evaluating and mitigating harmonic problems, but existing models still exhibit deficiencies in harmonic mechanism, model complexity and accuracy. This work proposes a calculation method of crossed frequency admittance matrix (CFAM) analytical model based on piecewise linearization, aiming to achieve accurate modeling of phase-controlled power electronic harmonic sources. Firstly, the traditional CFAM model construction methods are introduced, and the shortcomings in harmonic modeling are discussed. Subsequently, the parameter-solving process of the CFAM analytical model based on piecewise linearization is proposed. This method improves the accuracy of harmonic modeling and simplifies the construction process of the analytical model. Furthermore, taking single-phase and three-phase bridge rectifiers as examples, CFAM analytical models under intermittent and continuous load current conditions are established, respectively, and the unified harmonic models for both conditions are summarized. Finally, case studies of rectifier harmonic sources under varying circuit control parameters and supply voltage distortions are conducted through Matlab/Simulink and experiments. The results demonstrate that the proposed method provides higher accuracy and more stable performance for harmonic current estimation compared with the traditional CFAM model and constant current source model.

An increase in renewable energy generation in the microgrid can cause voltage oscillation problems. To address this issue, an equivalent circuit of the microgrid was established, including a synchronous generator, grid-connected inverter, and constant power load. Then, the impact of different renewable energy generation ratios, different direct current (DC) voltage loops, and phase-locked loop control bandwidths of the grid-connected inverter on microgrid stability were analyzed. The results indicate that an increase in the renewable energy generation ratio leads to a decrease in the stability margin of the microgrid. A microgrid stability improvement method involving the parallel connection of a virtual resistor with the grid-connected inverter was proposed. The resistance value of the virtual resistor was obtained through an adaptive algorithm. This method ensures the stable operation of the microgrid under different renewable energy generation ratios.

The integration of a high proportion of renewable energy sources has significantly increased the degree of power electronics penetration in power systems, leading to fundamental changes in the stability mechanisms and characteristics of novel power systems and making them highly susceptible to broadband oscillation phenomena. Broadband oscillations are characterized by high noise levels, wide frequency bands, strong nonlinearity, and strong time-varying properties, which restrict the accuracy and speed of current identification methods. To address this issue, this paper proposes a power system broadband oscillation identification method based on variational mode decomposition (VMD) and an improved probabilistic neural network-long short-term memory (PNN-LSTM) model. First, the VMD method is employed to adaptively determine the number of modal decompositions, suppress noise components, and monitor and identify effective information from oscillation signals. Meanwhile, a PNN is utilized to extract oscillation features from the input broadband oscillation signals and output the number of oscillation modes. Subsequently, the number of modes identified by the LSTM network is determined based on the modal count. Finally, taking a practical renewable energy power system in China as an example, the effectiveness of the proposed method is validated through online stability assessments under different operating conditions. Under the interference of Gaussian white noise, the identification accuracy of the proposed method for both the frequency and amplitude of wide-band oscillations remains above 97%. Compared with methods such as empirical mode decomposition and wavelet threshold denoising, the signal-to-noise ratio is improved to 45.86, the relative root mean square error is as low as 0.0024, and the overall identification accuracy reaches 98.74%.