Renewable energy sources (RESs) are expected to play a key role in supporting frequency stability in modern power systems, and thus, a variety of inertia-control methods have been developed. However, many RESs are installed behind the meter (BTM), and their data often remain inaccessible to the energy management systems (EMS) operated by utilities. As a result, their contributions to system inertia are frequently overlooked in stability evaluations. Although several inertia estimation methods have been proposed, most assume full availability of RES data. In practice, however, access to some data is often restricted to utilities, which limits the applicability of conventional methods, particularly for BTM-installed RESs. This article presents two data driven approaches for estimating the synthetic inertia and droop coefficients of BTM RESs, explicitly considering data availability under both normal and dynamic conditions. The first approach uses steady state EMS and RES data with different sampling intervals, while the second relies only on EMS dynamic data recorded during contingencies, avoiding dependence on RES measurements. Verification on the Jeju Island power system with practical measured EMS and RES data validate the proposed approaches and emphasize the influence of sampling intervals on estimation accuracy. The results show that the proposed methods provide an effective solution for assessing inertia and droop of BTM RESs with only limited data access, enabling utilities to conduct more reliable frequency stability analysis in low inertia grids.

Owing to the considerable shortage of traditional energy sources, utilization of renewable energy is gaining attention. Thus, the high-voltage inverters for solar and wind power generation systems are in huge demand. Modular inverters are more preferred over singular capacity inverters due to their numerous benefits such as relieving of thermal management, fault tolerance, reduced component stress, and modularity. This article proposes a parallel-configured improved Z-source inverter (ZSI) based on transformer. The proposed circuit has increased voltage gain with the combined benefits of paralleling inverters. Also, it offers lower harmonic distortion and lower filter requirements by employing the interleaved pulsewidth modulation strategy. Two parallel-connected transformer-based improved ZSI is first analyzed in detail, and the similar concept is then applied to N parallel-connected transformer-based improved ZSI. A hardware prototype is implemented in laboratory, and it is tested experimentally for high power applications.

This paper presents a novel high-frequency isolated full-bridge inverter. The output dc voltage of renewable energy sources varies in a wide range. To obtain a regulated output ac voltage, a buck-boost inverter is used. The proposed inverter provides step-up and step-down operation in a single stage with a wide range of input voltage. It is implemented with a single output inductor, a high-frequency transformer for isolation and only one switch is switching at high-frequency at a time. The proposed inverter eliminates the need for a 50/60 Hz low-frequency transformers and thus improve the power density. To verify the operation, a hardware prototype with output voltage 110 Vrms, line-frequency 60 Hz, and output power 500 W is implemented and tested.

To enhance performance of flash memory-based solid state disk (SSD), large logically chained blocks can be assembled by binding adjacent flash blocks across several flash memory chips. However, flash memory does not allow in-place overwriting and thus the operations that merge writes on these blocks suffer a visible decrease in performance. Furthermore, when small random writes are spread over the disk address space, performance tends to be degraded significantly. We thus present a technique to manage random writes efficiently to achieve stable SSD performance. In this paper, we propose a pattern adaptive SSD structure, which classifies access patterns as either random or sequential. The structure primarily consists of a write cache and a flash translation layer that separates groups of writes by access pattern (S-FTL). Separately managing the two types of write patterns enables greater parallelism and reduces the cost of large block management, thus enhancing the performance of the proposed SSD. Simulation experiments show that the proposed pattern adaptive structure can provide 39 percent decrease in extra flash block erase overhead on the average, and write performance can be improved by around 60 percent, compared with a basic FTL applied to existing parallel SSD structures.

This paper describes the Hessian matrix estimation of nonsmooth nonlinear parameters by the identifier based on a feedforward neural network (FFNN) embedded in a hybrid system, which is modeled by the differential-algebraic-impulsive-switched (DAIS) structure. After identifying full dynamics of the hybrid system, the FFNN is used to estimate second-order derivatives of an objective function J with respect to the nonlinear parameters from the gradient information, which are trajectory sensitivities. Then, the estimated Hessian matrix is applied to the optimal tuning of a saturation limiter used in a practical engineering system.

This paper describes the design of a nonlinear controller in a power system by using the reduced multivariate polynomial (RMP) optimization algorithm with the one-shot training property. The RMP model is applied to estimate its Hessian matrix in addition to identifying the trajectory sensitivities obtained from hybrid system modeling for the power system. In this paper, the saturation limiter of the power system stabilizer (PSS), which is an important nonlinear controller to improve low-frequency oscillation damping performance, is tuned optimally by using Hessian matrix estimated by the RMP model. The performance of the optimal output limits determined by the proposed method is evaluated by applying the large disturbance such as a three-phase short circuit to a power system.

This paper describes the optimal tuning for the output limits of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. The non-smooth nonlinear parameters such as the saturation limits of the PSS cannot be tuned by the conventional methods based on linear approaches. To implement the systematic optimal tuning for the output limits of the PSS, a feedforward neural network (FFNN) is applied to the hybrid system model based on the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is firstly designed to identify the trajectory sensitivities obtained from the DAIS structure. Thereafter, it estimates the second-order derivatives of an objective function J, which is used during iterations of optimization process. The performance of the optimal output limits tuned by the proposed method is evaluated by applying a large disturbance to a power system.

This paper proposes a power-aware cache block allocation algorithm for the way-selective set-associative cache on embedded systems to reduce energy consumption without additional delay or performance degradation. For this goal, way selection logic and specialized replacement policy are designed to enable only one way of set-associative cache as in the direct-mapped cache. Overall cache access time becomes almost the same as that of a conventional set associative cache with accessing additional way selection logic. Because data array can be accessed without waiting for tag comparison, multiplexer delay can be removed totally. The simulation result shows that the proposed architecture can reduce a per access power consumption by 59% over conventional set-associative caches with average 0.06% of negligible performance loss.

This paper compares two indirect adaptive neurocontrollers, namely a multilayer perceptron neurocontroller (MLPNC) and a radial basis function neurocontroller (RBFNC) to control a synchronous generator. The different damping and transient performances of two neurocontrollers are compared with those of conventional linear controllers, and analyzed based on the Lyapunov direct method.

A novel method for nonlinear identification of a static compensator connected to a power system using continually online trained (COT) artificial neural networks (ANNs) is presented in this paper. The identifier is successfully trained online to track the dynamics of the power network without any need for offline data and can be used in designing an adaptive neurocontroller for a static compensator connected to such system.

In this paper, the dual heuristic programming (DHP) optimization algorithm is used for the design of a nonlinear optimal neurocontroller that replaces the proportional-integral (PI) based conventional linear controller (CONVC) in the internal control of a power electronic converter based series compensator in the electric power transmission system. The performance of the proposed DHP based neurocontroller is compared with that of the CONVC with respect to damping low frequency oscillations. Simulation results using the PSCAD/EMTDC software package are presented.

The design of nonlinear optimal neurocontrollers based on the Adaptive Critic Designs (ACDs) family of algorithms has recently attracted interest. This paper presents a summary of these algorithms, and compares their performance when implemented on two different types of artificial neural networks, namely the multilayer perceptron neural network (MLPNN) and the radial basis function neural network (RBFNN). As an example for the application of the ACDs, the control of synchronous generator on an electric power grid is considered and results are presented to compare the different ACD family members and their implementations on different neural network architectures.