Computational method for remote meter calibration in power systems

ANALYSIS OF BAD DATA IN POWER SYSTEM STATE ESTIMATION UNDER NON-GAUSSIAN MEASUREMENT NOISE

As the power system changes in terms of smart grid devices, microgrids, and advancements in energy storage, it is useful to take advantage of power flow controllers. With power flow controllers a more dynamic and controllable power system will be possible. In this paper, it is suggested to use Quadrature Boosters (QBs) to better manage the power system. An IEEE 30-bus system is examined with the QB located in different lines. Since active and reactive losses are two different types of quantities a Performance Indicator, PI(α), was used to compare the two losses. The results were that an ideal location for the QB was on Line 1–3. The minimum PI was obtained here. Also, a second analysis was done with two QBs inserted in the IEEE 30-bus system to constrain bus voltages to tighter limits.

Increasing power system security is crucial to the future of the electric power grid. In this paper, we define a new class of cyberattacks to power systems-malicious modification of network data stored in an accessible database. Grounded in multivariate statistical process control, our approach to intrusion detection ensures data integrity in power system operations. We develop an algorithm that monitors power flow results and detects anomalies in the input values that could have been modified by cyberattacks. Our algorithm uses principal component analysis to separate power flow variability into regular and irregular subspaces. Analysis of the information in the irregular subspace determines whether the power system data has been compromised. We verify the efficacy of the algorithm using both the IEEE 24-bus and 118-bus reliability test systems. The results show that the developed algorithm is a promising enhancement to data security procedures in a control center.

Measurements from the electrical network are generally transmitted towards the control centres using special communication links. These measurements allow determining the state of the network in real time. However, these measurements often contain uncertainties due to the meter and communication errors, incomplete metering or unavailability of some of these measurements, etc. This paper presents the application of the Particle Swarm Optimization (PSO) algorithm in minimizing the raw measurement errors in order to identify or estimate the optimal operating state of the power system. Two different objective function formulations are assessed by PSO. The first formulation is the Weighted Least Square (WLS) and the second one is the Iteratively Reweighted Least Squares (IRLS) implementation of the Weighted Least Absolute Value (WLAV). Both solutions are compared with a Newton-Raphson (NR) power flow solution using an IEEE 6-bus test system.

Gerilim kararlılığı, güç sistemlerinde incelenmesi gereken önemli bir konudur. Sürekli artan elektrik enerjisi talebi, artan talebi karşılamak için yeni elektrik üretim santrallerinin kurulumu ve güç sistem arızaları gerilim kararlılığını olumsuz yönde etkilemektedir. Günümüzde gerilim kararlılığını iyileştirmek için güç sistemlerinde Esnek Alternatif Akım İletim Sistemleri (FACTS cihazları) kullanılmakta olup, ayrıca enerji talebini karşılamak ve gerilim kararlılığını iyileştirmek için güç sistemlerine Dağıtık Üretim Kaynakları (DGs) entegre edilmektedir. Bu çalışmada, IEEE 30 baralı güç sistemi ile Kuzeybatı Anadolu (KBA) güç sistemi, gerilim kararlılığı bakımından temel (nominal), aşırı yüklenme ve hat kopması durumlarında incelenmiştir. İncelenen güç sistemlerine FACTS cihazlarından Statik Var Kompanzatör (SVC) ve Tristör Kontrollü Seri Kapasitör (TCSC), DGs’lerden ise Yakıt Hücresi (YH), Güneş Enerji Santrali (GES) ve Rüzgâr Enerji Santrali (RES) ayrı ayrı ve birlikte bağlanmıştır. Güç sistemlerinin bara gerilim değerlerinde ve yüklenme parametresinde meydana gelen değişimler analiz edilmiştir.

Optimal generation units start-up sequence during restoration of power system considering network reliability using bi-level optimization

Maintaining reliability during power system operation relies heavily on the operator’s knowledge of the system and its current state. With the increasing complexity of power systems, full system monitoring is needed. Due to the costs to install and maintain measurement devices, a cost-effective optimal placement is normally employed, and as such, state estimation is used to complete the picture. However, in order to provide accurate state estimates in the current power system climate, the models must be fully expanded to include probabilistic uncertainties and non-linear assets. Recognizing its analogous relationship with state estimation, machine learning and its ability to summarily model unseen and complex relationships between input data is used. Thus, a power system state estimator was developed using modified long short-term (LSTM) neural networks to provide quicker and more accurate state estimates over the conventional weighted least squares-based state estimator (WLS-SE). The networks are then subject to standard polynomial scheduled weight pruning to further optimize the size and memory consumption of the neural networks. The state estimators were tested on a hybrid AC/DC distribution system composed of the IEEE 34-bus AC test system and a 9-bus DC microgrid. The conventional WLS-SE has achieved a root mean square error (RMSE) of 0.0151 p.u. for voltage magnitude estimates, while the LSTM’s were able to achieve RMSE’s between 0.0019 p.u. and 0.0087 p.u., with the latter having 75% weight sparsity, estimates about ten times faster, and half of its full memory requirement occupied.

This paper presents a mixed-integer linear robust multiobjective model for the expansion planning of an electric power system. An information-gap decision theory-based framework is proposed to take into account the uncertainties in electrical demand and new power system elements prices. The model is intended to increase the power system resistance against the uncertainties caused by forecast errors. The normal boundary intersection method is used to obtain the Pareto front of the multiobjective problem. Since the planning problem is a large-scale problem, the model is kept linear using the Big M linearization technique that is able to significantly decrease the computational burden. The fuel transportation and availability constraints are taken into account. The model also enables the system planner to build new fuel transportation routes whenever it is necessary. The generating units' retirement is also incorporated into the model, and the simulation results are showed to the advantages of incorporating units' retirement in the power system expansion planning model instead of considering it separately. The proposed multiobjective method is applied to the Garver 6-bus, IEEE 24-bus, and IEEE 118-bus test systems, and the results are compared with the well-known epsilon-constraint method.

Original scientific paper Reactive power optimization (RPO) is a major field of study to ensure power systems for operating in a secure and economical manner. RPO can be used for decreasing of active power losses, voltage control, and for the optimization of the power coefficients in power systems. The non-linear power loss function is used as an object function that will be minimized. In this study Chaotic Artificial Bee Colony (CABC) algorithm is used to minimize the active power loss of power systems. Chaotic maps such as logistic map and Henon map are used against the random number generator. CABC is applied on the IEEE6-bus and IEEE 30-bus test systems and the results are shown. Accordingly, the results have been evaluated and observed that the stability critical values found by CABC can be used to produce good potential solutions. Simulation results are promising and show the effectiveness of the applied approach.

This chapter describes the impacts that time delays in feedback have on small signal as well as transient stability of power systems. We present a power system model comprising of delay differential-algebraic equations (DDAEs) and describe general techniques to compute the spectrum of DDAE and to integrate such equations in time domain. The focus is on delays arising in measured signals, e.g., remote frequency measurements for power system stabilizers (PSS) of synchronous machines. Several examples based on a benchmark system, the IEEE 14-bus test system, as well as a real-world system are discussed and analysed.

This paper deals with a simple technique for measurement placement method of power system state estimation. The minimum condition number of the measurement matrix is used as the criteria in conjunction with sequential elimination to generalize the measurement placement. The singular value decomposition (SVD) approach will be used to solve the state estimation. The simulation study is performed on the IEEE 14-bus test system by linear weighted least square (WLS). It is found that, this algorithm can give a solution of measurement placement of injection current and voltage that make the power system observable.

This paper presents a new methodology based on cuckoo search algorithm (CSA) for enhancing the performance of interline power flow controller (IPFC) under multiline transmission. In this approach we perform priority process through the contingency analysis for finding the severity of the line outage. For determining the severity, bus voltage limit violation, overloading of lines and power loss are taken into account. We are applying CSA to determine the optimal location and transmission congestion management of IPFC. An adaptive proportional integral derivative with filter (PIDF) controller is developed to control the deviation and power flow. The simulation is performed using MATLAB for two case studies using an IEEE 118-bus and IEEE 300-bus test system. Simulation results indicate that the location and parameters optimized by using CSA, minimizing the bus voltage abuse, congestion management, and overloaded lines. This method is designed in such a way that it improves the security of the power system. For further improvement of the system, the IPFC is placed in tie-line. The performance of PIDF controller has been studied under different conditions and the simulation result shows that PIDF performs better than PID controller. The performance of CSA is compared with other two optimization techniques such as particle swarm optimization (PSO) and genetic algorithm (GA) under both the IEEE 118-bus and IEEE 300-bus test systems. The result shows that the proposed CSA outperforms the other two models and it best suits the power system security.

Guest Editorial: Situational awareness of integrated energy systems

Because of uncertainties of renewable generation resources and load, efficient tools are required for load flow analysis in power systems. Many of the existing papers assume a set of given probability density functions (PDFs) to model uncertainties and develop parametric probabilistic load flow tools. However, the uncertainties might not fall in any standard class of PDFs. Thus, nonparametric tools are needed. This paper presents a method for nonparametric probabilistic load flow analysis to determine PDFs of the load flow outputs. The proposed method is based on the mean value first order saddle point approximation. For a system with $\boldsymbol {n}$ random variables, $\boldsymbol {(n+1)}$ load flow calculations are utilized to establish first order Taylor series expansion and then saddle point approximation is adopted to determine the probabilistic characteristic of desired output variables. The proposed nonparametric estimator provides accurate results while needing a reasonable computational effort. The estimator needs only a limited available data set of random variables without requiring information whether the data set belongs to a certain class of parametric distribution functions. Furthermore, both probability and cumulative distribution functions of load flow outputs are directly established without using integral or differential operators. The proposed method is tested on the IEEE 14-bus and the IEEE 118-bus test systems and promising results are obtained.

A novel two-stage optimization method for the reliability based security constraints unit commitment in presence of wind units