Optimal PMU Placement Using Genetic Algorithm for 330kV 52-Bus Nigerian Network

The phasor Measurement Unit is a modern tracking tool mounted on a network to track and manage power systems. PMU is accurate and time-synchronized device that gives voltage phasor measurements in nodes and current phasor measurements connected to those nodes where the PMU is installed. This study introduces the Genetic Algorithm for optimization of allocation of PMUs to enable maximum observation of the power network. The optimal PMU placement (OPP) problem is developed to minimize the quantity of PMU to be placed. The set and optimized model can efficiently position PMU in any network, considering the regular operation and zero injection (ZIN). Thus, the allocation algorithm implemented on IEEE 14-bus systems, the result was compared to that of existing works which achieved the same system of redundancy index. As a further study, the proposed approach is applied to the Nigerian 330kV new 52-bus systems, under operational arrangements for maximum observability of the network system. The technique formulated to handle normal operation and zero injection node succeeded in producing comparable results with other available techniques

Power System State Estimation and Renewable Energy Optimization in Smart Grids

The future smart grid will benefit from real-time monitoring, automated outage management, increased renewable energy penetration, and enhanced consumer involvement. Among the many research areas related to smart grids, this dissertation will focus on two important topics: power system state estimation using phasor measurement units (PMUs), and optimization for renewable energy integration. In the first topic, we consider power system state estimation using PMUs, when phase angle mismatch exists in the measurements. In particular, we build a measurement model that takes into account the measurement phase angle mismatch. We then propose algorithms to increase state estimation accuracy by taking into account the phase angle mismatch. Based on the proposed measurement model, we derive the posterior Cramér-Rao bound on the estimation error, and propose a method for PMU placement in the grid. Using numerical examples, we show that by considering the phase angle mismatch in the measurements, the estimation accuracy can be significantly improved compared with the traditional weighted least-squares estimator or Kalman filtering. We also show that using the proposed PMU placement strategy can increase the estimation accuracy by placing a limited number of PMUs in proper locations. In the second topic, we consider optimization for renewable energy integration in smart grids. We first consider a scenario where individual energy users own on-site renewable generators, and can both purchase and sell electricity to the main grid. Under this setup, we develop a method for parallel load scheduling of different energy users, with the goal of reducing the overall cost to energy users as well as to energy providers. The goal is achieved by finding the optimal load schedule of each individual energy user in a parallel distributed manner, to flatten the overall load of all the energy users. We then consider the case of a micro-grid, or an isolated grid, with a large penetration of renewable energy. In this case, we jointly optimize the energy storage and renewable generator capacity, in order to ensure an uninterrupted power supply with minimum costs. To handle the large dimensionality of the problem due to large historical datasets used, we reformulate the original optimization problem as a consensus problem, and use the alternating direction method of multipliers to solve for the optimal solution in a distributed manner

Minimal Driver Nodes for Structural Controllability of Large-Scale Dynamical Systems: Node Classification

This paper considers the problem of minimal control inputs to affect the system states such that the resulting system is structurally controllable. This problem and the dual problem of minimal observability are claimed to have no polynomial-order exact solution and, therefore, are NP-hard. Here, adopting a graph-theoretic approach, this problem is solved for general nonlinear (and also structure-invariant) systems and a P-order solution is proposed. In this direction, the dynamical system is modeled as a directed graph, called \textit{system digraph}, and two types of graph components are introduced which are tightly related with structural controllability. Two types of nodes which are required to be affected (or driven) by an input, called \textit{driver nodes}, are defined, and minimal number of these driver nodes are obtained. Polynomial-order complexity of the given algorithms to solve the problem ensures applicability of the solution for analysis of large-scale dynamical systems. {The structural results in this paper are significant as compared to the existing literature which offer approximate and computationally less-efficient, e.g. Gramian-based, solutions for the problem, while this paper provides exact solution with lower computational complexity and applicable for controllability analysis of nonlinear systems.Comment: accepted at IEEE Systems Journa

Cyber-attack and reliability monitoring of the synchrophasor smart grid network

The recent advancement of synchrophasor measurements technology in the conventional power grid can monitor and control the state variables of the network very accurately at a high sampling rate in real-time. The complete observability of system states can be achieved through the Phasor Measurement Unit (PMU). The inclusion of a zero injection bus (ZIB) optimized the total number of PMU requirements for complete observation of the synchrophasor network. The communication channels between measurement devices and control centers are highly vulnerable to cyber threats. Thus, an anomaly that occurs with PMU devices during a cyber-attack can affect the system’s reliability. Therefore, monitoring the reliability of the synchrophasor network has become essential for healthy power operation. Synchrophasor measurement technology can enhance wide-area surveillance and security functionality. However, the dependability of such technologies in the context of information network accessibility has yet to be investigated in a coherent model. Growing electric grid defence levels to mitigate the impact of cyber-attacks is essential. The cumulative effect of synchrophasor network observability and reliability is discussed in this paper by optimizing the number of PMUs deployed and the interruption load that occurs during an anomaly with PMU while taking ZIB into account.The backup PMU deployment modeling is also presented to secure the reliability and observability of the grid network during an anomaly occurs with PMU. The indices, Interrupted Load Probability Index (ILPI) and Expected Demand Not Supplied (EDNS), are used to evaluate the reliability of synchrophasor grid networks by integrating the state probability of PMU unavailability due to cyber intrusion

電力系統の静的および動的セキュリティ評価増強のための同期位相計測装置の最適配置

九州工業大学博士学位論文 学位記番号：工博甲第490号 学位授与年月日：令和2年3月25日1 INTRODUCTION|2 PMU-BASED POWER SYSTEM MONITORING AND CONTROL|3 OPTIMAL PMU PLACEMENT PROBLEM AND STATE ESTIMATION|4 MULTI OBJECTIVE PMU PLACEMENT WITH CURRENT CHANNEL SELECTION|5 INFLUENCE OF MEASUREMENT UNCERTAINTY PROPAGATION IN PMU PSEUDO MEASUREMENT|6 PHASOR-ASSISTED VOLTAGE STABILITY ASSESSMENT BASED ON OPTIMALLY PLACED PMUS|7 PMU PLACEMENT FOR DYNAMIC VULNERABILITY ASSESSMENT|8 CONCLUSIONS九州工業大学令和元年

Applications of Computational Intelligence to Power Systems

In power system operation and control, the basic goal is to provide users with quality electricity power in an economically rational degree for power systems, and to ensure their stability and reliability. However, the increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control, and automation. In the liberalised electricity market, the operation and control of a power system has become a complex process because of the complexity in modelling and uncertainties. Computational intelligence (CI) is a family of modern tools for solving complex problems that are difficult to solve using conventional techniques, as these methods are based on several requirements that may not be true all of the time. Developing solutions with these “learning-based” tools offers the following two major advantages: the development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information, known as uncertainty