The growth in adoption of communication technologies in the power system results in an increase in its vulnerability towards cyberattacks. This paper presents a novel state partition-particle filter (SP-PF) based detection algorithm that dynamically adapts to varying operating conditions. The algorithm partitions state variables into size-restricted blocks, effectively grouping highly correlated variables to enhance computational efficiency and detection accuracy. Our approach consists of two main steps: (i) state-partition estimation of variables and (ii) detection based on likelihood conditions. The proposed detection algorithm was tested in a real-time cyber-physical environment using a real-time digital simulator (RTDS) in hardware-in-loop configuration with PMUs and a synchronization clock, all connected via standard TCP/UDP protocols. Experimental results demonstrate successful detection of false data injection attacks, replay attacks, and hybrid attacks under various operating conditions. Comparative analysis with extended Kalman filter shows that our approach achieves significantly improved accuracy in state estimation with reduced mean square error, enhancing the overall robustness of the detection mechanism.

In this article, a machine-learning-based fault detection and classification method is proposed. Two supervised learning-based protection modules are developed for the relays considered in the study—one to detect the fault and discriminate between the symmetrical or unsymmetrical nature of the fault and another to detect the faulty phase(s). A robust set of features using both relay voltage and current signals is utilized for developing the modules. The features are obtained using the multiresolution decomposition based on the empirical wavelet transform. The modules are tested on the unbalanced IEEE 13-node network integrated with inverter-based distributed generation systems capable of reactive power injection in the low-voltage ride-through mode of operation. Varying penetration levels and intermittent output of distributed generation, fault resistance, fault inception time, noise in the signals, and switching events occurring around the fault period are some of the unique operating scenarios considered to demonstrate the consistent performance of the relays. The simulations are performed in power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC) and the protection modules are developed in MATLAB. The performance of the protection modules is evaluated using several metrics with respect to various classes of events occurring in the network.

Due to their superior reliability, efficiency, and robustness as compared to unipolar dc grid systems, bipolar dc grid systems are quickly gaining traction for renewable energy integration. However, progress in developing multiport converters resulting in lower costs, more compact designs, and higher power density in bipolar microgrid systems has been slow. As a result, a new isolated multiport dc-dc converter with bipolar inherently symmetric outputs (MIBDC) is proposed in this study. The suggested converter has a competitive advantage over its few counterparts in terms of the number of input ports, voltage gain, and natural symmetry of the outputs. Furthermore, because the proposed MIBDC uses a fixed transformer with only one primary and secondary winding for any number of inputs, it considerably decreases component count and control complexity. The proposed converter's operation is quantitatively tested in simulation and on OPAL-OP5700 RT's hardware-in-the-loop (HIL) validation platform for independent and simultaneous power transfer from multiple sources with varying voltages.

An isolated multiple input dc-dc converter (MIC) with unidirectional buck-boost characteristics and simultaneous power transfer is proposed for multi-sources in renewable energy systems in this paper. When compared to existing isolated MICs, the proposed MIC significantly reduces the component count and control complexity since it requires a fixed coupled inductor with only one primary and secondary winding each for any number of inputs and does not require any phase-shifted pulse-width modulation. The operation of the proposed converter for simultaneous power transfer from multiple sources with varying voltages is numerically verified in simulation and validated on OPAL-RT’s OP5700 hardware-in-the-loop (HIL) validation platform.

This Editorial Submission is for SS on Deep Learning and Data Analytics to Support the Smart Grid Operation with Renewable Energy. Out of 81 submissions in SS, only 12 have been finally accepted to be published in this Transactions.

The future power network will be designed to accommodate and integrate all types of distributed renewable energy resources, storage units, and flexible demand response loads in the existing conventional grid. Also it should be able to perform intelligent energy management utilizing advancement in computation and communication to cater the needs of ever growing energy demand in secure manner. This leads the transition of conventional power grid into smart grid. However, performance of the smart grid utilizing automated, intelligent, and integrated functional blocks with widely interconnected distributed energy resources is dependent on advanced communication network, sensors, computing, and information technologies. There is a need for reliable and efficient communication network and computing infrastructure for the robust, affordable, and secure supply of electric power through smart grid operation. This Special Section has accepted altogether seven research manuscripts depending on the novelties and problems that they address. The paper are briefly summarized here.

The potential application of signal processing techniques is not only to detect the event but also to characterize them according to physical disturbance. In this paper, event detection and its characterization algorithm is presented. The event detection scheme uses computation of spectral kurtosis on sum of intrinsic mode functions. The algorithm is capable of detecting the event in phasor measurement units data by comparing the maximum energy and root-mean square of energy content of present analysis segment with respect to previous segment. The statistical indices applied are capable to flag specific data and thus the timely detection of events. Further, statistical features extracted from event-related segment suggest that the transient signals from different regions are distinct and thus can be classified. The signal characterization is further represented in terms of short-term energy and group delay. The analysis on event triggered signal demonstrates the related physical phenomenon in each event type. The study suggests the most relevant signal associated with a particular type of event.

In this paper, the quality of service of communication infrastructure implemented in multiarea power system for load frequency control application is assessed in smart grid environment. In this study, network induced effects time delay, packet loss, bandwidth, quantization, and change in communication topology (CT) has been addressed to examine the system performance in closed loop. Uncertainty in time delay is approximated using deterministic and stochastic models. The network is modeled considering different network configurations, i.e., change in CT. The modeled communication network guarantees control relevant properties, i.e., stability. The decentralized controller and linear matrix inequality-based linear quadratic regulator is implemented to reduce the dynamic performance (mean square error of states variables) of power system as CT changes. Simulation results suggest that the proposed scheme of networked control is superior with respect to other design methods available in the literature, and thus robust to communication imperfections.

This paper addresses the modeling of hydro power plant dynamics using neural network approach. The cost function as root mean square error is optimized by particle swarm optimization technique. The identification performance is compared with fuzzy models based on GK clustering algorithm in application to study hydro power plant dynamics. It is found that the response obtained from the NN model is comparable to those determined by fuzzy model with much significance to nature of input-output variables used for modeling.

This paper addresses the appropriate structure selection of linear-in parameter model for speed identification of hydro turbine. The genetic programming (GP) approach with parameters determined by orthogonal least square (OLS) is adopted in the study. The simulation of second order H infinity turbine penstock dynamic transfer function in closed loop with random load variation is performed to generate data for model structure determination. The data generated from among the available PID variants, forward rectangular controller in conjunction with online identification algorithm, LSMADF provided the best model structure.