Cellular Computational Network Research Articles

Distributed Volt-Var Curve Optimization Using a Cellular Computational Network Representation of an Electric Power Distribution System

Voltage control in modern electric power distribution systems has become challenging due to the increasing penetration of distributed energy resources (DER). The current state-of-the-art voltage control is based on static/pre-determined DER volt-var curves. Static volt-var curves do not provide sufficient flexibility to address the temporal and spatial aspects of the voltage control problem in a power system with a large number of DER. This paper presents a simple, scalable, and robust distributed optimization framework (DOF) for optimizing voltage control. The proposed framework allows for data-driven distributed voltage optimization in a power distribution system. This method enhances voltage control by optimizing volt-var curve parameters of inverters in a distributed manner based on a cellular computational network (CCN) representation of the power distribution system. The cellular optimization approach enables the system-wide optimization. The cells to be optimized may be prioritized and two methods namely, graph and impact-based methods, are studied. The impact-based method requires extra initial computational efforts but thereafter provides better computational throughput than the graph-based method. The DOF is illustrated on a modified standard distribution test case with several DERs. The results from the test case demonstrate that the DOF based volt-var optimization results in consistently better performance than the state-of-the-art volt-var control.

Denial of Service Attack on Tie-Line Bias Control in a Power System With PV Plant

The use of synchrophasor networks consisting of phasor measurement units (PMUs) makes it possible to monitor, analyzes, and control the electric power grid in real-time. PMU measurements of frequencies, currents, voltages, and phase angles are transmitted to system control centers through synchrophasor networks. Delayed or missing measurements from PMUs in closed-loop applications could lead to power system instability. Although the use of virtual private network (VPN) tunnels eliminates many security vulnerabilities, VPNs are still vulnerable to denial of service (DoS) attack that exploits a side-channel vulnerability. In this paper, the authors detail their analysis of DoS attack on a two-area four machine power system with a utility-scale photovoltaic (PV) plant. Automatic generation control (AGC) is used to implement a tie-line bias control in one of the areas. The impact of PMU packet dropping on AGC operation and countermeasures are presented in this paper. Cellular computational network (CCN) prediction of PMU data is used to implement a virtual synchrophasor network (VSN). The data from VSN is used by the AGC when the PMU packets are disrupted by DoS attacks. Real-time experimental results show the CCN based VSN effectively inferred the missing data and mitigated the negative impacts of DoS attacks.

A hybrid method for power system state estimation using Cellular Computational Network

Several heuristic optimization methods including Particle Swarm Optimization (PSO) have been studied for power system state estimation and these perform quite well for small systems. However, in case of larger systems with hundreds of states, these suffer from the curse of dimensionality. To overcome this problem, a hybrid state estimator that consists of a Cellular Computational Network (CCN) and the Genetic Algorithm (GA) is proposed in this study. CCN is a framework that distributes the whole computation to computation cells and the cells execute local estimation. The result of CCN is further improved using GA. To compare the performance of the proposed estimator, two acclaimed variants of PSO, Comprehensive Learning PSO, and Orthogonal Learning PSO, which are specialized in multimodal high dimensional systems, are also implemented both individually and in conjunction with CCN. Through simulation, it is shown that the proposed CCN-GA outperform all direct and hybrid methods in terms of accuracy. Typical results on an IEEE 16-machine 68-bus power system are presented to illustrate the effectiveness of the CCN-GA over other methods.

Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system

To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results.

Computational Approaches for Bad Data Handling in Power System Synchrophasor Networks

Abstract The state estimators used in real-time power system control centers now process bad data as a standard routine. With the introduction and deployment of phasor measurement units (PMUs), it is possible to model power systems, even with their time-varying nature, in real-time. However, PMUs remain vulnerable to providing bad data for several reasons. In this paper, a new intelligent framework, the cellular computational netwo rk (CCN), is introduced for the decentralized predictive modeling and dynamic state estimation (DSE) of a power system with PMU data. The CCN-based DSE is resilient to interactions between multiple segments of bad data from one or more PMUs.