<p>U ovoj doktorskoj disertaciji predložena je nova metoda za detekciju malicioznih napada injektiranjem loših merenja na elektroenergetski sistem. Predloženi algoritam baziran je na sinergiji statičke i dinamičke estimacije stanja, i u stanju je da detektuje ovaj tip napada u realnom vremenu, za najkritičniji scenario gde napadač ima potpuno znanje o sistemu, i neograničen pristup resursima.</p> / <p>This PhD thesis proposes a novel method for detection of malicious false data<br />injection attacks on power system. The proposed algorithm is based on<br />synergy between static and dynamic state estimators, and is capable of<br />detecting the forementioned attacks in real time, for the most critical scenarios,<br />where an attacker has complete knowledge about the compromised power<br />system and unlimited resources to stage an attack.</p>

Submitted by isabela.moljf@hotmail.com (isabela.moljf@hotmail.com) on 2016-08-12T12:25:16Z No. of bitstreams: 1 tamiresgomesmoreira.pdf: 4532594 bytes, checksum: 64d45fbe56c5ece94a7586a1d27d46df (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-08-15T13:22:42Z (GMT) No. of bitstreams: 1 tamiresgomesmoreira.pdf: 4532594 bytes, checksum: 64d45fbe56c5ece94a7586a1d27d46df (MD5) / Made available in DSpace on 2016-08-15T13:22:42Z (GMT). No. of bitstreams: 1 tamiresgomesmoreira.pdf: 4532594 bytes, checksum: 64d45fbe56c5ece94a7586a1d27d46df (MD5) Previous issue date: 2016-03-10 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Esta dissertação apresenta uma nova metodologia para detecção e identificação de erros grosseiros no processo de estimação de estados para sistemas de distribuição de energia elétrica com topologia radial, usando Unidades de Medição Fasorial, conhecidas como PMUs (Phasor Measurement Units). O algoritmo de estimação de estados considera todas as correntes passantes nas linhas do sistema, expressas em coordenadas retangulares, como estadosaseremestimados. Osvaloresmedidosserãofasoresdetensãoecorrenteaquisitados pelas PMUs. A fim de restaurar a observabilidade do sistema com poucas unidades de medição serão considerados dados históricos de potência ativa/reativa demandada para as barras não monitoradas por PMUs, disponibilizados pelas concessionárias de energia elétrica. Esses valores serão considerados como restrições de desigualdade variando entre limites mínimos e máximos em um problema de otimização não linear cujo objetivo é minimizar a soma dos quadrados dos resíduos, sendo esses a diferença entre o valor da grandeza medida pela PMU e o seu correspondente valor estimado, ponderado por suas respectivas covariâncias. Baseado nos valores de corrente estimados, outras grandezas elétricas podem ser calculadas utilizando leis de Kirchhoff. Considerando a topologia radial dos alimentadores de distribuição, a proposta para o processamento de erros grosseiros consiste na divisão da rede elétrica com topologia radial em vários subsistemas, visando reduzir o esforço computacional associado ao processo de estimaçãodeestados. Ametodologiaapresentadaserádivididaeabordadaemduasetapas. A primeira se refere à detecção de erros grosseiros, sendo avaliada pelo valor da FOB para cada subsistema, onde valores acima de um determinado valor limítrofe preestabelecido para cada uma das FOBs indicam a presença de medidas com erros grosseiros. Já a segunda, baseia-se na identificação da PMU responsável por aquisitar medições com erros grosseiros e pauta-se na abordagem por barras fictícias, barras estas em que a potência demandada é nula. Os resultados obtidos são validados através do uso de sistemas testes encontrados na literatura. O problema de otimização é solucionado pelo Método de Pontos Interiores com Barreira de Segurança (Safety Barrier Interior Point Method). / This dissertation presents a novel methodology for bad data detection and identification in the State Estimation process for electrical power distribution systems with radial topology, using Phasor Measurement Units (PMUs). The state estimation algorithm considers all branch currents of the system, expresssed in rectangular coordinates, as states to be estimated. The measured values will be phasors acquisited by the PMUs. In order to make the system fully observable with few measurement units, it will be considered historical data of active/reactive power demand for the non-monitored buses, provided by the electrical utilities. These values will be considered as inequality constraints varying between minimum and maximum limits in a non-linear optimization problem which aims to minimize the sum of the squared of the residuals considering the residual being the difference between the measured values by the PMUs and their corresponding estimated values, weighted by its corresponding covariances. Based on the estimated branch currents values, other electrical quantities can be calculated by Kirchhoff’s laws. Consideringtheradialtopology,theproposedapproachforthebaddataprocessingconsists on the electrical network partitioning into various subsystems, which aims to reduce the computational effort associated to the states estimation process. The methodology presented in this work for bad data processing will be divided and implemented into two steps. The first part refers to the bad data detection and it is evaluated by the objective function value for each subsystem, in which high values indicate the presence of bad data. The second part relies on the identification of the PMU which is responsible for acquisitioningbaddataanditisaddressedintwodifferentways. Thefirstoneisaddressed for a single subsystem (single feeder) and is based on the creation of fictitious buses, which will be buses with null power demand. The obtained results are validated by using test systems found in the literature. The optimization problem is solved by the Safety Barrier Interior Point Method.

Power system state estimation (PSSE) is an energy management system function responsible for the computation of the most likely values of state variables viz., bus voltage magnitudes and angles. The state estimation is obtained within a network at a given instant by solving a system of mostly non-linear equations whose parameters are the redundant measurements, both static such as transformer/line parameters and dynamic such as, status of circuit breakers/isolators, transformer tap positions, active/reactive power flows, generator active/reactive power outputs etc. PSSE involves solving an over determined set of nonlinear equations by minimizing a weighted norm of the measurement residuals. Typically, the L1 and L2 norms are employed. The use of L2 norm leads to state estimation based on the weighted least squares (WLS) criterion. This method is known to exhibit efficient filtering capability when the errors are Gaussian but fails in the case of presence of bad data. The method of hypothesis testing identification can be incorporated into the WLS estimator to detect and identify bad data. Nevertheless, it is prone to failure when the measurement is a leverage point. On the other hand state estimation based on the weighted least absolute value (WLAV) criterion using L1 norm, has superior bad data suppression capability. But it also fails in rejecting bad data measurements associated with leverage points. Leverage points are highly influential measurements that attract the state estimator solution towards them. Consequently, much research effort has focused recently, on producing a LAV estimator that remains robust in the presence of bad leverage measurements. This problem has been addressed in the thesis work. Two methods, which aims development of robust estimator that are insensitive to bad leverage points, have been proposed viz., (i) The objective function used here is obtained by linearizing L2 norm of the error function. In addition to the constraints corresponding to measurement set, constraints corresponding to bounds of state variables are also involved. Linear programming (LP) optimization is carried out using upper bound optimization technique. (ii) A hybrid optimization algorithm which is combination of”upper bound optimization technique” and ”an improved algorithm for discrete l1 linear approximation”, to restrict the state variables not to leave the basis during optimization process. Linear programming optimization, with bounds of state variables as additional constraints is carried out using the proposed hybrid optimization algorithm. The proposed state estimator algorithms are tested on 24-bus EHV equivalent of southern power network, 36-bus EHV equivalent of western grid, 205-bus interconnected grid system of southern region and IEEE-39 bus New England system. Performances of the proposed two methods are compared with the WLAV estimator in the presence of bad data associated with leverage points. Also, the effect of bad leverage measurements on the interacting bad data, which are non-leverage, has been compared. Results show that proposed state estimator algorithms rejects bad data associated with leverage points efficiently.