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J = create_jacobian_matrix(Ybus+Ybus_ssc_not_controllable+Ybus_vsc_not_controllable, V, ref, refpvpq, pvpq, pq, createJ, pvpq_lookup, nref, npv, npq, numba, slack_weights, dist_slack) # if tdpf: # # p.u. values for T, a1, a2, I, S # # todo: distributed_slack works fine if sn_mva is rather high (e.g. 1000), otherwise no convergence. Why? # J = create_J_tdpf(branch, tdpf_lines, alpha_pu, r_ref_pu, refpvpq if dist_slack else pvpq, pq, pvpq_lookup, # pq_lookup, tau, tdpf_delay_s, Vm, Va, r_theta_pu, J, r, x, g) if any_facts_controllable or tdpf: K_J = vstack([eye(J.shape[0], format="csr"), csr_matrix((num_facts_controllable + len(tdpf_lines), J.shape[0]))], format="csr") J = K_J * J * K_J.T # this extends the J matrix with 0-rows and 0-columns if tdpf: J_m_tdpf = create_J_tdpf(branch, tdpf_lines, alpha_pu, r_ref_pu, refpvpq if dist_slack else pvpq, pq, pvpq_lookup, pq_lookup, tau, tdpf_delay_s, Vm, Va, r_theta_pu, J, r, x, g) J = J + J_m_tdpf if any_svc: J_m_svc = create_J_modification_svc(J, svc_buses, refpvpq if dist_slack else pvpq, pq, pq_lookup, Vm, x_control_svc, svc_x_l_pu, svc_x_cvar_pu, num_svc_controllable, svc_controllable) J = J + J_m_svc
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